Qiskit_AdaptVQE.ipynb

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      "name": "Qiskit_AdaptVQE.ipynb",
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2Ufk0eZN0Lc"
      },
      "source": [
        "%%capture\n",
        "!pip install qiskit[visualization]\n",
        "#!pip install pycsf not working?\n",
        "%pip install pyscf==1.7.5\n",
        "!pip install openfermion\n",
        "!pip install openfermionpyscf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ja4qC1Q8lg2C"
      },
      "source": [
        "%%capture\n",
        "import scipy\n",
        "import pyscf\n",
        "import openfermion\n",
        "import openfermionpyscf \n",
        "import itertools\n",
        "import qiskit\n",
        "import numpy as np\n",
        "import copy\n",
        "import pickle\n",
        "\n",
        "from google.colab import files\n",
        "from openfermionpyscf import run_pyscf\n",
        "from openfermion.utils import count_qubits, hermitian_conjugated\n",
        "from openfermion.linalg import jw_hartree_fock_state\n",
        "from openfermion.circuits import simulate_trotter\n",
        "from openfermion.transforms import (\n",
        "    jordan_wigner, get_fermion_operator, normal_ordered\n",
        ")\n",
        "from openfermion import (\n",
        "    get_sparse_operator, get_ground_state, FermionOperator,\n",
        "    jw_get_ground_state_at_particle_number, MolecularData,\n",
        "    expectation, uccsd_convert_amplitude_format,\n",
        "    get_interaction_operator, QubitOperator, eigenspectrum,\n",
        "    InteractionOperator, FermionOperator\n",
        ")\n",
        "\n",
        "from qiskit import (Aer, QuantumCircuit, ClassicalRegister, QuantumRegister, IBMQ,\n",
        "                   execute, transpile)\n",
        "from qiskit.circuit import QuantumCircuit, ParameterVector, Parameter\n",
        "from qiskit.algorithms.optimizers import COBYLA\n",
        "from qiskit.providers.ibmq import least_busy\n",
        "from qiskit.test.mock import FakeBelem\n",
        "from qiskit.providers.aer import QasmSimulator\n",
        "from qiskit.quantum_info import purity\n",
        "from qiskit.providers.aer.noise.device import (\n",
        "    basic_device_gate_errors, gate_error_values\n",
        ")\n",
        "from qiskit.providers.aer.noise import (\n",
        "    NoiseModel,thermal_relaxation_error, pauli_error, ReadoutError\n",
        ")\n",
        "\n",
        "from qiskit.aqua import QuantumInstance\n",
        "from qiskit.aqua.algorithms import VQE, NumPyMinimumEigensolver\n",
        "from qiskit.aqua.operators import I, Z, X, Y\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2f39vDPy0R1",
        "outputId": "10df48f2-1597-4509-a08c-603045e3f068"
      },
      "source": [
        "IBMQ.enable_account(\"88757d22d1747c543664fb35d8789fac1c1271ff43dd365e62f067f3e34ab5c4d95f0ed7155eb5685a871a3d3b69eb34a57eece5c139b070062c8396ddc87d66\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cThBylHU3xht"
      },
      "source": [
        "provider = IBMQ.get_provider(hub='ibm-q-minho')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqH_z6vymZ-D"
      },
      "source": [
        "## Function Definitions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqC4XOOyfzPV"
      },
      "source": [
        "# Define chemical accuracy\n",
        "chemicalAccuracy = 1.5936*10**-3\n",
        "\n",
        "# Define necessary Pauli operators (two-dimensional) as matrices\n",
        "pauliX = np.array([[0,1],\n",
        "                 [1,0]],\n",
        "                dtype = complex)\n",
        "pauliZ = np.array([[1,0],\n",
        "                 [0,-1]],\n",
        "                dtype = complex)\n",
        "pauliY = np.array([[0,-1j],\n",
        "                 [1j,0]],\n",
        "                dtype = complex)\n",
        "\n",
        "def stringToMatrix(pauliString):\n",
        "  '''\n",
        "  Converts a Pauli string to its matrix form.\n",
        "\n",
        "  Arguments:\n",
        "    pauliString (str): the Pauli string (e.g. \"IXYIZ\")\n",
        "\n",
        "  Returns:\n",
        "    matrix (np.ndarray): the corresponding matrix, in the computational basis\n",
        "\n",
        "  '''\n",
        "\n",
        "  matrix = np.array([1])\n",
        "\n",
        "  # Iteratively construct the matrix, going through each single qubit Pauli term\n",
        "  for pauli in pauliString:\n",
        "      if pauli == \"I\":\n",
        "        matrix = np.kron(matrix,np.identity(2))\n",
        "      elif pauli == \"X\":\n",
        "        matrix = np.kron(matrix,pauliX)\n",
        "      elif pauli == \"Y\":\n",
        "        matrix = np.kron(matrix,pauliY)\n",
        "      elif pauli == \"Z\":\n",
        "        matrix = np.kron(matrix,pauliZ)\n",
        "\n",
        "  return matrix\n",
        "  \n",
        "def fromVectortoKet(stateVector):\n",
        "  '''\n",
        "  Transforms a vector representing a basis state to the corresponding ket.\n",
        "\n",
        "  Arguments:\n",
        "    stateVector (np.ndarray): computational basis vector in the 2^n dimensional \n",
        "      Hilbert space\n",
        "\n",
        "  Returns:\n",
        "    ket (list): a list of length n representing the corresponding ket \n",
        "  '''\n",
        "\n",
        "  dim = len(stateVector)\n",
        "  ket = []\n",
        "\n",
        "  while dim>1:\n",
        "    if any (stateVector[i] for i in range(int(dim/2))):\n",
        "      # Ket is of the form |0>|...>. \n",
        "\n",
        "      #Fix |0> as the msq.\n",
        "      ket.append(0)\n",
        "\n",
        "      # Get the vector representing the state of the remaining qubits.\n",
        "      stateVector = stateVector[:int(dim/2)]\n",
        "\n",
        "    else:\n",
        "      # Ket is of the form |1>|...>. \n",
        "      \n",
        "      #Fix |0> as the msq.\n",
        "      ket.append(1)\n",
        "\n",
        "      # Get the vector representing the state of the remaining qubits.\n",
        "      stateVector = stateVector[int(dim//2):]\n",
        "\n",
        "    dim = dim/2\n",
        "\n",
        "  return ket\n",
        "\n",
        "def fromKettoVector(ket):\n",
        "  '''\n",
        "  Transforms a ket representing a basis state to the corresponding state vector.\n",
        "\n",
        "  Arguments:\n",
        "    ket (list): a list of length n representing the ket \n",
        "\n",
        "  Returns:\n",
        "    stateVector (np.ndarray): the corresponding basis vector in the \n",
        "      2^n dimensional Hilbert space\n",
        "  '''\n",
        "  stateVector = [1]\n",
        "  \n",
        "  # Iterate through the ket, calculating the tensor product of the qubit states\n",
        "  for i in ket:\n",
        "    qubitVector = [not i,i]\n",
        "    stateVector = np.kron(stateVector,qubitVector)\n",
        "\n",
        "  return stateVector\n",
        "\n",
        "def slaterDeterminantToKet(index, dimension):\n",
        "  '''\n",
        "  Transforms a Slater Determinant (computational basis state) into\n",
        "    the corresponding ket.\n",
        "\n",
        "  Arguments:\n",
        "    index (int): the index of the non-zero element of the computational\n",
        "      basis state.\n",
        "    dimension (int): the dimension of the Hilbert space\n",
        "  \n",
        "  Returns:\n",
        "    ket (list): the corresponding ket as a list of length dimension\n",
        "    \n",
        "  '''\n",
        "  \n",
        "  vector = [0 for _ in range (index)]+[1]+[1 for _ in range (dimension-index-1)]\n",
        "  ket = fromVectortoKet(vector)\n",
        "\n",
        "  return ket\n",
        "  \n",
        "def getHartreeFockKet(electronNumber,qubitNumber):\n",
        "  '''\n",
        "  Get the Hartree Fock determinant, as a list in big endian representing the ket\n",
        "  |1>|1>...|0>|0>.\n",
        "\n",
        "  Arguments:\n",
        "    electronNumber (int): the number of electrons of the molecule.\n",
        "    qubitNumber (int): the number of qubits necessary to represent the molecule\n",
        "      (equal to the number of spin orbitals we're considering active).\n",
        "\n",
        "  Returns:\n",
        "    referenceDeterminant (list): a list of lenght qubitNumber, representing the \n",
        "      ket of the adequate computational basis state in big-endian ordering.\n",
        "  '''\n",
        "\n",
        "  # Consider occupied the lower energy orbitals, until enough one particle \n",
        "  #states are filled\n",
        "  referenceDeterminant = [1 for _ in range(electronNumber)]\n",
        "\n",
        "  # Consider the remaining orbitals empty\n",
        "  referenceDeterminant += [0 for _ in range(qubitNumber-electronNumber)]\n",
        "\n",
        "  return referenceDeterminant\n",
        "\n",
        "def calculateOverlap(stateCoordinates1,stateCoordinates2):\n",
        "    '''\n",
        "    Calculates the overlap between two states, given their coordinates.\n",
        "\n",
        "    Arguments:\n",
        "      stateCoordinates1 (np.ndarray): the coordinates of one of the states in \n",
        "        some orthonormal basis,\n",
        "      stateCoordinates2 (np.ndarray): the coordinates of the other state, in \n",
        "        the same basis\n",
        "\n",
        "    Returns: \n",
        "      overlap (float): the overlap between two states (absolute value of the \n",
        "        inner product).\n",
        "    '''\n",
        "\n",
        "    bra = np.conj(stateCoordinates1)\n",
        "    ket = stateCoordinates2\n",
        "    overlap = np.abs(np.dot(bra,ket))\n",
        "    \n",
        "    return overlap\n",
        "\n",
        "def findSubStrings(mainString,hamiltonian,checked = []):\n",
        "    '''\n",
        "    Finds and groups all the strings in a Hamiltonian that only differ from \n",
        "    mainString by identity operators.\n",
        "\n",
        "    Arguments:\n",
        "      mainString (str): a Pauli string (e.g. \"XZ)\n",
        "      hamiltonian (dict): a Hamiltonian (with Pauli strings as keys and their \n",
        "        coefficients as values)\n",
        "      checked (list): a list of the strings in the Hamiltonian that have already\n",
        "        been inserted in another group\n",
        "\n",
        "    Returns: \n",
        "      groupedOperators (dict): a dictionary whose keys are boolean strings \n",
        "        representing substrings of the mainString (e.g. if mainString = \"XZ\", \n",
        "        \"IZ\" would be represented as \"01\"). It includes all the strings in the \n",
        "        hamiltonian that can be written in this form (because they only differ \n",
        "        from mainString by identities), except for those that were in checked\n",
        "        (because they are already part of another group of strings).\n",
        "      checked (list):  the same list passed as an argument, with extra values\n",
        "        (the strings that were grouped in this function call).\n",
        "    '''\n",
        "    \n",
        "    groupedOperators = {}\n",
        "    \n",
        "    # Go through the keys in the dictionary representing the Hamiltonian that \n",
        "    #haven't been grouped yet, and find those that only differ from mainString \n",
        "    #by identities\n",
        "    for pauliString in hamiltonian:\n",
        "        \n",
        "        if pauliString not in checked:\n",
        "            # The string hasn't been grouped yet\n",
        "            \n",
        "            if(all((op1 == op2 or op2 == \"I\") \\\n",
        "                   for op1,op2 in zip(mainString,pauliString))):\n",
        "                # The string only differs from mainString by identities\n",
        "                \n",
        "                # Represent the string as a substring of the main one\n",
        "                booleanString = \"\".join([str(int(op1 == op2)) for op1,op2 in \\\n",
        "                                       zip(mainString,pauliString)])\n",
        "                    \n",
        "                # Add the boolean string representing this string as a key to \n",
        "                #the dictionary of grouped operators, and associate its \n",
        "                #coefficient as its value\n",
        "                groupedOperators[booleanString] = hamiltonian[pauliString]\n",
        "                \n",
        "                # Mark the string as grouped, so that it's not added to any \n",
        "                #other group\n",
        "                checked.append(pauliString)\n",
        "                \n",
        "    return (groupedOperators,checked)\n",
        "\n",
        "def groupHamiltonian(hamiltonian):\n",
        "    '''\n",
        "    Organizes a Hamiltonian into groups where strings only differ from \n",
        "    identities, so that the expectation values of all the strings in each \n",
        "    group can be calculated from the same measurement array.\n",
        "\n",
        "    Arguments: \n",
        "      hamiltonian (dict): a dictionary representing a Hamiltonian, with Pauli \n",
        "        strings as keys and their coefficients as values.\n",
        "\n",
        "    Returns: \n",
        "      groupedHamiltonian (dict): a dictionary of subhamiltonians, each of \n",
        "        which includes Pauli strings that only differ from each other by \n",
        "        identities. \n",
        "        The keys of groupedHamiltonian are the main strings of each group: the \n",
        "        ones with least identity terms. The value associated to a main string is \n",
        "        a dictionary, whose keys are boolean strings representing substrings of \n",
        "        the respective main string (with 1 where the Pauli is the same, and 0\n",
        "        where it's identity instead). The values are their coefficients.\n",
        "    '''\n",
        "    groupedHamiltonian = {}\n",
        "    checked = []\n",
        "    \n",
        "    # Go through the hamiltonian, starting by the terms that have less\n",
        "    #identity operators\n",
        "    for mainString in \\\n",
        "        sorted(hamiltonian,key = lambda pauliString: pauliString.count(\"I\")):\n",
        "            \n",
        "        # Call findSubStrings to find all the strings in the dictionary that \n",
        "        #only differ from mainString by identities, and organize them as a \n",
        "        #dictionary (groupedOperators)\n",
        "        groupedOperators,checked = findSubStrings(mainString,hamiltonian,checked)\n",
        "        \n",
        "        # Use the dictionary as a value for the mainString key in the \n",
        "        #groupedHamiltonian dictionary\n",
        "        groupedHamiltonian[mainString] = groupedOperators\n",
        "        \n",
        "        # If all the strings have been grouped, exit the for cycle\n",
        "        if(len(checked) == len(hamiltonian.keys())):\n",
        "           break\n",
        "       \n",
        "    return groupedHamiltonian\n",
        "\n",
        "def convertHamiltonian(openfermionHamiltonian):\n",
        "  '''\n",
        "  Formats a qubit Hamiltonian obtained from openfermion, so that it's a suitable\n",
        "  argument for functions such as measureExpectationEstimation.\n",
        "\n",
        "  Arguments:\n",
        "    openfermionHamiltonian (openfermion.qubitOperator): the Hamiltonian.\n",
        "\n",
        "  Returns:\n",
        "    formattedHamiltonian (dict): the Hamiltonian as a dictionary with Pauli\n",
        "      strings (eg 'YXZI') as keys and their coefficients as values.\n",
        "  '''\n",
        "\n",
        "  formattedHamiltonian = {}\n",
        "  qubitNumber = count_qubits(openfermionHamiltonian)\n",
        "\n",
        "  # Iterate through the terms in the Hamiltonian\n",
        "  for term in openfermionHamiltonian.get_operators():\n",
        "\n",
        "    operators = []\n",
        "    coefficient = list(term.terms.values())[0]\n",
        "    pauliString = list(term.terms.keys())[0]\n",
        "    previousQubit = -1\n",
        "\n",
        "    for (qubit,operator) in pauliString:\n",
        "\n",
        "      # If there are qubits in which no operations are performed, add identities \n",
        "      #as necessary, to make sure that the length of the string will match the \n",
        "      #number of qubits\n",
        "      identities = (qubit-previousQubit-1)\n",
        "      if identities>0: \n",
        "        operators.append('I'*identities)\n",
        "\n",
        "      operators.append(operator)\n",
        "      previousQubit = qubit\n",
        "    \n",
        "    # Add final identity operators if the string still doesn't have the \n",
        "    #correct length (because no operations are performed in the last qubits)\n",
        "    operators.append('I'*(qubitNumber-previousQubit-1))\n",
        "\n",
        "    formattedHamiltonian[\"\".join(operators)] = coefficient\n",
        "\n",
        "  return formattedHamiltonian\n",
        "\n",
        "def hamiltonianToMatrix(hamiltonian):\n",
        "    '''\n",
        "    Convert a Hamiltonian (from OpenFermion) to matrix form.\n",
        "    \n",
        "    Arguments:\n",
        "      hamiltonian (openfermion.InteractionOperator): the Hamiltonian to be\n",
        "        transformed.\n",
        "\n",
        "    Returns:\n",
        "      matrix (np.ndarray): the Hamiltonian, as a matrix in the computational \n",
        "        basis\n",
        "    \n",
        "    ''' \n",
        "    \n",
        "    qubitNumber = hamiltonian.n_qubits\n",
        "    \n",
        "    hamiltonian = jordan_wigner(hamiltonian)\n",
        "\n",
        "    formattedHamiltonian = convertHamiltonian(hamiltonian)\n",
        "    groupedHamiltonian = groupHamiltonian(formattedHamiltonian)\n",
        "\n",
        "    matrix = np.zeros((2**qubitNumber,2**qubitNumber),dtype = complex)\n",
        "\n",
        "    # Iterate through the strings in the Hamiltonian, adding the respective \n",
        "    #contribution to the matrix\n",
        "    for string in groupedHamiltonian:\n",
        "\n",
        "      for substring in groupedHamiltonian[string]:\n",
        "        pauli = (\"\".join(\"I\"*(not int(b)) + a*int(b) \\\n",
        "                         for (a,b) in zip(string,substring)))\n",
        "        \n",
        "        matrix += stringToMatrix(pauli) * groupedHamiltonian[string][substring]\n",
        "\n",
        "    return matrix\n",
        "\n",
        "def stateEnergy(stateCoordinates,hamiltonian):\n",
        "    ''' \n",
        "    Calculates the exact energy in a specific state.\n",
        "\n",
        "    Arguments:\n",
        "      stateCoordinates (np.ndarray): the state in which to obtain the \n",
        "        expectation value.\n",
        "      hamiltonian (dict): the Hamiltonian of the system.\n",
        "    \n",
        "    Returns:\n",
        "      exactEnergy (float): the energy expecation value in the state.\n",
        "    ''' \n",
        "\n",
        "    exactEnergy = 0\n",
        "    \n",
        "    # Obtain the theoretical expectation value for each Pauli string in the\n",
        "    #Hamiltonian by matrix multiplication, and perform the necessary weighed\n",
        "    #sum to obtain the energy expectation value.\n",
        "    for pauliString in hamiltonian:\n",
        "        \n",
        "        ket = np.array(stateCoordinates,dtype = complex)\n",
        "        bra = np.conj(ket)\n",
        "        \n",
        "        ket = np.matmul(stringToMatrix(pauliString),ket)\n",
        "        expectationValue = np.real(np.dot(bra,ket))\n",
        "        \n",
        "        exactEnergy+=\\\n",
        "            hamiltonian[pauliString]*expectationValue\n",
        "            \n",
        "    return exactEnergy\n",
        "\n",
        "def exactStateEnergySparse(stateVector,sparseHamiltonian):\n",
        "    ''' \n",
        "    Calculates the exact energy in a specific state, using sparse matrices.\n",
        "\n",
        "    Arguments:\n",
        "      stateVector (Union[np.ndarray, scipy.sparse.csc_matrix): the state in \n",
        "        which to obtain the expectation value.\n",
        "      sparseHamiltonian (scipy.sparse.csc_matrix): the Hamiltonian of the system.\n",
        "    \n",
        "    Returns:\n",
        "      energy (float): the energy expecation value in the state.\n",
        "    ''' \n",
        "\n",
        "    if not isinstance(stateVector,scipy.sparse.csc_matrix):\n",
        "      ket = scipy.sparse.csc_matrix(stateVector,dtype=complex).transpose()\n",
        "    else:\n",
        "      ket = stateVector\n",
        "      \n",
        "    bra = ket.transpose().conj()\n",
        "\n",
        "    energy = (bra * sparseHamiltonian * ket)[0,0].real\n",
        "    \n",
        "    return energy\n",
        "\n",
        "def pauliToCircuit(operator,parameter,circuit,qubitNumber): \n",
        "  '''\n",
        "  Creates the circuit for applying e^ (j * operator * parameter), for 'operator'\n",
        "  a single Pauli string.\n",
        "  Uses little endian endian, as Qiskit requires.\n",
        "\n",
        "  Arguments:\n",
        "    operator (union[openfermion.QubitOperator, openfermion.FermionOperator,\n",
        "      openfermion.InteractionOperator]): the operator to be simulated\n",
        "    parameter (qiskit.circuit.parameter): the variational parameter\n",
        "    circuit (qiskit.circuit.QuantumCircuit): the circuit to add the gates to\n",
        "    qubitNumber (int): the number of qubits in the circuit\n",
        "  '''\n",
        "\n",
        "  # If operator is an InteractionOperator, shape it into a FermionOperator\n",
        "  if isinstance(operator,InteractionOperator):\n",
        "    operator = get_fermion_operator(operator)\n",
        "\n",
        "  # If operator is a FermionOperator, use the Jordan Wigner transformation\n",
        "  #to map it into a QubitOperator\n",
        "  if isinstance(operator,FermionOperator):\n",
        "    operator = jordan_wigner(operator)\n",
        "\n",
        "  # Isolate the Pauli string (term should only have one)\n",
        "  pauliString = list(operator.terms.keys())[0]\n",
        "\n",
        "  # Keep track of the qubit indices involved in this particular Pauli string.\n",
        "  # It's necessary so as to know which are included in the sequence of CNOTs \n",
        "  #that compute the parity\n",
        "  involvedQubits = []\n",
        "\n",
        "  # Perform necessary basis rotations\n",
        "  for pauli in pauliString:\n",
        "\n",
        "    # Get the index of the qubit this Pauli operator acts on\n",
        "    qubitIndex = pauli[0]\n",
        "    involvedQubits.append(qubitIndex)\n",
        "\n",
        "    # Get the Pauli operator identifier (X,Y or Z)\n",
        "    pauliOp = pauli[1]\n",
        "\n",
        "    if pauliOp == \"X\":\n",
        "      # Rotate to X basis\n",
        "      # In big endian the argument would be qubitIndex\n",
        "      circuit.h(qubitNumber - 1 - qubitIndex)\n",
        "\n",
        "    if pauliOp == \"Y\":\n",
        "      # Rotate to Y Basis\n",
        "      circuit.rx(np.pi/2,qubitNumber - 1 - qubitIndex)\n",
        "\n",
        "  # Compute parity and store the result on the last involved qubit\n",
        "  for i in range(len(involvedQubits)-1):\n",
        "\n",
        "    control = involvedQubits[i]\n",
        "    target = involvedQubits[i+1]\n",
        "\n",
        "    circuit.cx(qubitNumber - 1 - control,qubitNumber - 1 - target)\n",
        "  \n",
        "  # Apply e^(i*Z*parameter) = Rz(-parameter*2) to the last involved qubit\n",
        "  lastQubit = max(involvedQubits)\n",
        "  circuit.rz(-2 * parameter,qubitNumber - 1 - lastQubit)\n",
        "\n",
        "  # Uncompute parity\n",
        "  for i in range(len(involvedQubits)-2,-1,-1):\n",
        "\n",
        "    control = involvedQubits[i]\n",
        "    target = involvedQubits[i+1]\n",
        "\n",
        "    circuit.cx(qubitNumber - 1 - control,qubitNumber - 1 - target)\n",
        "\n",
        "  # Undo basis rotations\n",
        "  for pauli in pauliString:\n",
        "\n",
        "    # Get the index of the qubit this Pauli operator acts on\n",
        "    qubitIndex = pauli[0]\n",
        "\n",
        "    # Get the Pauli operator identifier (X,Y or Z)\n",
        "    pauliOp = pauli[1]\n",
        "\n",
        "    if pauliOp == \"X\":\n",
        "      # Rotate to Z basis from X basis\n",
        "      circuit.h(qubitNumber - 1 - qubitIndex)\n",
        "\n",
        "    if pauliOp == \"Y\":\n",
        "      # Rotate to Z basis from Y Basis\n",
        "      circuit.rx(-np.pi/2,qubitNumber - 1 - qubitIndex)\n",
        "\n",
        "def toAquaObservable(operator):\n",
        "  observable = 0\n",
        "\n",
        "  for pauliString in operator:\n",
        "\n",
        "    transformedPauli = 1\n",
        "    for pauli in pauliString:\n",
        "      if pauli == \"I\":\n",
        "        transformedPauli = transformedPauli ^ I\n",
        "      elif pauli == \"X\":\n",
        "        transformedPauli = transformedPauli ^ X\n",
        "      elif pauli == \"Y\":\n",
        "        transformedPauli = transformedPauli ^ Y\n",
        "      elif pauli == \"Z\":\n",
        "        transformedPauli = transformedPauli ^ Z\n",
        "\n",
        "\n",
        "    coefficient = operator[pauliString]\n",
        "    observable += transformedPauli * coefficient\n",
        "\n",
        "  return observable"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_I7NdyTYmkch"
      },
      "source": [
        "## Molecule Definitions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPNcPicHlYCa"
      },
      "source": [
        "# H2\n",
        "geometry = [['H',[0,0,0]],['H',[0,0,0.74]]]\n",
        "basis = 'sto-3g'\n",
        "multiplicity = 1\n",
        "charge = 0\n",
        "h2 = MolecularData(geometry,basis,multiplicity,charge,description='H2')\n",
        "h2m = run_pyscf(h2,run_fci = True,run_ccsd = True)\n",
        "\n",
        "# HeH+\n",
        "r = 1 # interatomic distance in angstrom\n",
        "geometry = [['He',[0,0,0]],['H',[0,0,r]]]\n",
        "basis = 'sto-3g'\n",
        "multiplicity = 1\n",
        "charge = +1\n",
        "helonium = MolecularData(geometry,basis,multiplicity,charge,description='HeH+')\n",
        "helonium = run_pyscf(helonium,run_fci = True,run_ccsd = True)\n",
        "\n",
        "# LiH\n",
        "bondLength = 1.45 # interatomic distance in angstrom\n",
        "geometry = [['Li',[0,0,0]],['H',[0,0,bondLength]]]\n",
        "basis = 'sto-3g'\n",
        "multiplicity = 1\n",
        "charge = 0\n",
        "liH = MolecularData(geometry,basis,multiplicity,charge,description='LiH')\n",
        "liH = run_pyscf(liH,run_fci = True,run_ccsd = True)\n",
        "\n",
        "# Alternative to using run_pyscf: load from OpenFermion (data for this \n",
        "#particular molecule at this particular interatomic distance is available in \n",
        "#a file that comes with OF)\n",
        "liHOF = MolecularData(geometry,basis,multiplicity,charge,description = '1.45')\n",
        "liHOF.load()\n",
        "\n",
        "# H4\n",
        "r = 1.5\n",
        "geometry = [('H', (0,0,0)), ('H', (0,0,r)), ('H', (0,0,2*r)), \n",
        "            ('H', (0,0,3*r))]\n",
        "basis = 'sto-3g'\n",
        "multiplicity = 1\n",
        "charge = 0\n",
        "h4 = MolecularData(geometry,basis,multiplicity,charge,description='H4')\n",
        "h4 = run_pyscf(h4,run_fci = True,run_ccsd = True)\n",
        "\n",
        "# H6\n",
        "r = 1.5\n",
        "geometry = [('H', (0,0,0)), ('H', (0,0,r)), ('H', (0,0,2*r)), \n",
        "            ('H', (0,0,3*r)), ('H', (0,0,4*r)), ('H', (0,0,5*r))]\n",
        "basis = 'sto-3g'\n",
        "multiplicity = 1\n",
        "charge = 0\n",
        "h6 = MolecularData(geometry,basis,multiplicity,charge,description='H6')\n",
        "h6 = run_pyscf(h6,run_fci = True,run_ccsd = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nT60NNP0mniZ"
      },
      "source": [
        "## Choosing the Molecule"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pynw8Rtwl5HT"
      },
      "source": [
        "molecule = h2\n",
        "exactEnergy = molecule.fci_energy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqJ_LU9LmB5s"
      },
      "source": [
        "name = molecule.description\n",
        "bondLength = molecule.geometry[1][1][2]\n",
        "\n",
        "qubitNumber = molecule.n_qubits\n",
        "electronNumber = molecule.n_electrons\n",
        "orbitalNumber = molecule.n_orbitals"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S3tE_Y9KZGVv"
      },
      "source": [
        "## Defining the Operator Pool"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZ0tOG9fV3Qb"
      },
      "source": [
        "Code for creating the fermionic pool (singlet_gsd) taken from [the GitHub repository with the original Adapt-VQE implementation](https://github.com/mayhallgroup/adapt-vqe)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8rQxneWssGkV",
        "outputId": "536494e4-2da9-4ef4-f83c-655609080519"
      },
      "source": [
        "singlet_gsd = []\n",
        "\n",
        "for p in range(0,orbitalNumber):\n",
        "    pa = 2*p\n",
        "    pb = 2*p+1\n",
        "\n",
        "    for q in range(p,orbitalNumber):\n",
        "        qa = 2*q\n",
        "        qb = 2*q+1\n",
        "\n",
        "        termA =  FermionOperator(((pa,1),(qa,0)))\n",
        "        termA += FermionOperator(((pb,1),(qb,0)))\n",
        "\n",
        "        termA -= hermitian_conjugated(termA)\n",
        "        termA = normal_ordered(termA)\n",
        "\n",
        "        #Normalize\n",
        "        coeffA = 0\n",
        "        for t in termA.terms:\n",
        "            coeff_t = termA.terms[t]\n",
        "            coeffA += coeff_t * coeff_t\n",
        "\n",
        "        if termA.many_body_order() > 0:\n",
        "            termA = termA/np.sqrt(coeffA)\n",
        "            singlet_gsd.append(termA)\n",
        "\n",
        "\n",
        "pq = -1\n",
        "for p in range(0,orbitalNumber):\n",
        "    pa = 2*p\n",
        "    pb = 2*p+1\n",
        "\n",
        "    for q in range(p,orbitalNumber):\n",
        "        qa = 2*q\n",
        "        qb = 2*q+1\n",
        "\n",
        "        pq += 1\n",
        "\n",
        "        rs = -1\n",
        "        for r in range(0,orbitalNumber):\n",
        "            ra = 2*r\n",
        "            rb = 2*r+1\n",
        "\n",
        "            for s in range(r,orbitalNumber):\n",
        "                sa = 2*s\n",
        "                sb = 2*s+1\n",
        "\n",
        "                rs += 1\n",
        "\n",
        "                if(pq > rs):\n",
        "                    continue\n",
        "\n",
        "                termA =  FermionOperator(((ra,1),(pa,0),(sa,1),(qa,0)), 2/np.sqrt(12))\n",
        "                termA += FermionOperator(((rb,1),(pb,0),(sb,1),(qb,0)), 2/np.sqrt(12))\n",
        "                termA += FermionOperator(((ra,1),(pa,0),(sb,1),(qb,0)), 1/np.sqrt(12))\n",
        "                termA += FermionOperator(((rb,1),(pb,0),(sa,1),(qa,0)), 1/np.sqrt(12))\n",
        "                termA += FermionOperator(((ra,1),(pb,0),(sb,1),(qa,0)), 1/np.sqrt(12))\n",
        "                termA += FermionOperator(((rb,1),(pa,0),(sa,1),(qb,0)), 1/np.sqrt(12))\n",
        "\n",
        "                termB =  FermionOperator(((ra,1),(pa,0),(sb,1),(qb,0)),  1/2.0)\n",
        "                termB += FermionOperator(((rb,1),(pb,0),(sa,1),(qa,0)),  1/2.0)\n",
        "                termB += FermionOperator(((ra,1),(pb,0),(sb,1),(qa,0)), -1/2.0)\n",
        "                termB += FermionOperator(((rb,1),(pa,0),(sa,1),(qb,0)), -1/2.0)\n",
        "\n",
        "                termA -= hermitian_conjugated(termA)\n",
        "                termB -= hermitian_conjugated(termB)\n",
        "\n",
        "                termA = normal_ordered(termA)\n",
        "                termB = normal_ordered(termB)\n",
        "\n",
        "                #Normalize\n",
        "                coeffA = 0\n",
        "                coeffB = 0\n",
        "                for t in termA.terms:\n",
        "                    coeff_t = termA.terms[t]\n",
        "                    coeffA += coeff_t * coeff_t\n",
        "                for t in termB.terms:\n",
        "                    coeff_t = termB.terms[t]\n",
        "                    coeffB += coeff_t * coeff_t\n",
        "\n",
        "\n",
        "                if termA.many_body_order() > 0:\n",
        "                    termA = termA/np.sqrt(coeffA)\n",
        "                    singlet_gsd.append(termA)\n",
        "\n",
        "                if termB.many_body_order() > 0:\n",
        "                    termB = termB/np.sqrt(coeffB)\n",
        "                    singlet_gsd.append(termB)\n",
        "\n",
        "print(\"Pool size:\",len(singlet_gsd))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pool size: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQrzUz3Nsagd",
        "outputId": "1ce6326d-5b35-4802-fb87-d34d0c656e14"
      },
      "source": [
        "pool = singlet_gsd\n",
        "\n",
        "qubitPool = []\n",
        "\n",
        "for fermionOp in pool:\n",
        "\n",
        "  qubitOp = jordan_wigner(fermionOp)\n",
        "  \n",
        "  for pauli in qubitOp.terms:\n",
        "\n",
        "    qubitOp = QubitOperator(pauli,1j)\n",
        "\n",
        "    if qubitOp not in qubitPool:\n",
        "      qubitPool.append(qubitOp)\n",
        "\n",
        "print(\"Pool Size:\",len(qubitPool))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pool Size: 20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "woJ4S64BZDri"
      },
      "source": [
        "## AdaptData class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RzBD5YU4mfYz"
      },
      "source": [
        "class AdaptData:\n",
        "  '''\n",
        "  Class meant to store data from an Adapt VQE run. \n",
        "\n",
        "  Methods:\n",
        "    processIteration: to be called by the AdaptVQE class at the end of each \n",
        "      iteration\n",
        "    close: to be called by the AdaptVQE class at the end of the run\n",
        "    plot: to be called to plot data after the run \n",
        "  '''\n",
        "\n",
        "  def __init__(self,\n",
        "               initialEnergy,\n",
        "               pool,\n",
        "               sparsePool,\n",
        "               referenceDeterminant,\n",
        "               backend,\n",
        "               shots,\n",
        "               previousData = None):\n",
        "    '''\n",
        "    Initialize class instance\n",
        "\n",
        "    Arguments:\n",
        "      initialEnergy (float): energy of the reference state\n",
        "      pool (list): operator pool\n",
        "      sparsePool (list): sparse version of the pool\n",
        "      referenceDeterminant (list): the Slater determinant to be used as reference,\n",
        "        in big endian ordering (|abcd> <-> [a,b,c,d]; [qubit 0, qubit 1,...])\n",
        "      backend (Union[None,qiskit.providers.ibmq.IBMQBackend]): the backend to \n",
        "        be used. If none, a simulation will be run using sparse matrices.\n",
        "      shots (int): number of circuit repetitions\n",
        "      previousData (AdaptData): data from a previous run, to be used as a \n",
        "        starting point\n",
        "    '''\n",
        "\n",
        "    self.pool = pool\n",
        "    self.sparsePool = sparsePool\n",
        "    self.backend = qinstance.backend\n",
        "    self.shots = shots\n",
        "    \n",
        "    if previousData is not None:\n",
        "      # Starting point is the result of a previous Adapt run\n",
        "\n",
        "      self.initialEnergy = initialEnergy\n",
        "      self.evolution = copy.deepcopy(previousData.evolution)\n",
        "\n",
        "      # Make sure the data corresponds to a complete run\n",
        "      if not previousData.result:\n",
        "        raise ValueError(\"Previous data does not supply final results.\")\n",
        "\n",
        "      self.current = copy.deepcopy(previousData.result)\n",
        "      self.iterationCounter = previousData.iterationCounter\n",
        "\n",
        "      # Make sure we're using the same pool\n",
        "      # Comparing lenght is good enough\n",
        "      if previousData is not None:\n",
        "        assert(len(self.sparsePool) == len(previousData.sparsePool))\n",
        "\n",
        "      '''\n",
        "      assert(self.iterationCounter == len(self.evolution[\"selected gradient\"]))\n",
        "      assert(self.iterationCounter == len(self.evolution[\"total norm\"\"]))\n",
        "      assert(self.iterationCounter == len(self.evolution[\"coefficients\"]))\n",
        "      assert(self.iterationCounter == len(self.current[\"ansatz\"]))\n",
        "      assert(self.iterationCounter == len(self.current[\"coefficients\"]))\n",
        "      assert(self.iterationCounter == len(self.current[\"indices\"]))\n",
        "      '''\n",
        "\n",
        "    else:\n",
        "      # The initial energy is stored apart from the remaining ones, so that the \n",
        "      #index of the gradient norms in the beginning of each iteration \n",
        "      #corresponds to the index of the coefficients and energies in that \n",
        "      #iteration in the respective arrays.\n",
        "      self.initialEnergy = initialEnergy\n",
        "\n",
        "      self.evolution = {}\n",
        "      self.evolution[\"energies\"] = []\n",
        "      self.evolution[\"total norm\"] = []\n",
        "      self.evolution[\"selected gradient\"] = []\n",
        "      self.evolution[\"coefficients\"] = []\n",
        "      self.evolution[\"energyChange\"] = []\n",
        "      self.evolution[\"indices\"] = []\n",
        "\n",
        "      self.current = {}\n",
        "      self.current[\"ansatz\"] = []\n",
        "      self.current[\"coefficients\"] = []\n",
        "      self.current[\"indices\"] = []\n",
        "      # performance of a selected operator = | energy change / gradient | \n",
        "      self.current[\"ansatz performances\"] = []\n",
        "      self.current[\"performances\"] = []\n",
        "      self.current[\"energy\"] = None\n",
        "      self.current[\"state\"] = None\n",
        "      self.current[\"total norm\"] = None\n",
        "\n",
        "      self.iterationCounter = 0\n",
        "\n",
        "    self.closed = False\n",
        "    self.success = False\n",
        "\n",
        "    assert(self.iterationCounter == len(self.evolution[\"selected gradient\"]))\n",
        "    assert(self.iterationCounter == len(self.evolution[\"total norm\"]))\n",
        "    assert(self.iterationCounter == len(self.evolution[\"coefficients\"]))\n",
        " \n",
        "    self.result = {}\n",
        "\n",
        "  def processIteration(self,\n",
        "                   operatorIndex,\n",
        "                   operator,\n",
        "                   energy,\n",
        "                   totalNorm,\n",
        "                   selectedGradient,\n",
        "                   coefficients):\n",
        "    '''\n",
        "    Receives and processes the values fed to it by an instance of the AdaptVQE \n",
        "    class at the end of each run.\n",
        "\n",
        "    Arguments:\n",
        "      operatorIndex (int): index of the selected operator\n",
        "      operator (union[openfermion.QubitOperator, openfermion.FermionOperator]):\n",
        "        the selected operator\n",
        "      energy (float): the optimized energy, at the end of the iteration\n",
        "      totalNorm (int): the norm of the total gradient norm at the beggining \n",
        "        of this iteration\n",
        "      selectedGradient (float): the absolute value of the gradient of the \n",
        "        operator that was added in this iteration\n",
        "      coefficients (list): a list of the coefficients selected by the optimizer\n",
        "        in this iteration\n",
        "    '''\n",
        "\n",
        "    if not isinstance(energy,float):\n",
        "      raise TypeError(\"Expected float, not {}.\".format(type(energy).__name__))\n",
        "    if not isinstance(totalNorm,float):\n",
        "      raise TypeError(\"Expected float, not {}.\".format(type(totalNorm).__name__))\n",
        "    if not isinstance(selectedGradient,float):\n",
        "      raise TypeError(\"Expected float, not {}.\".format(type(selectedGradient).__name__))\n",
        "    if not isinstance(coefficients,list):\n",
        "      raise TypeError(\"Expected list, not {}.\".format(type(coefficients).__name__))\n",
        "\n",
        "    if len(coefficients) != len(self.current[\"ansatz\"]) + 1:\n",
        "      raise ValueError(\"The length of the coefficient list should match the\"\n",
        "      \" ansatz size ({} != {}).\".format\n",
        "        (len(coefficients),len(self.current[\"ansatz\"]) + 1))\n",
        "\n",
        "    if totalNorm < 0:\n",
        "      raise ValueError(\"Total gradient norm should be positive; its {}\".\\\n",
        "                       format(totalNorm))\n",
        "      \n",
        "    self.current[\"coefficients\"] = copy.deepcopy(coefficients)\n",
        "    \n",
        "    if self.iterationCounter == 0:\n",
        "      previousEnergy = self.initialEnergy\n",
        "    else:\n",
        "      previousEnergy = self.current[\"energy\"] \n",
        "\n",
        "    energyChange = energy - previousEnergy\n",
        "    performance = np.abs(energyChange / selectedGradient)\n",
        "\n",
        "    self.current[\"ansatz performances\"].append(performance)\n",
        "    self.current[\"performances\"].append(performance)\n",
        "\n",
        "    ansatzPerformance = np.average(self.current[\"ansatz performances\"])\n",
        "\n",
        "    print(\"Energy Change: \",energyChange)\n",
        "    print(\"Performance ratio: \",performance)\n",
        "    print(\"\\nCurrent average performance ratio: \", \n",
        "          np.average(self.current[\"performances\"]))\n",
        "    print(\"Current 10-last average performance ratio: \", \n",
        "          np.average(self.current[\"performances\"][-10:]))\n",
        "    print(\"Current average performance ratio of the ansatz: \",\n",
        "          ansatzPerformance)\n",
        "\n",
        "    self.current[\"ansatz\"].append(operator)\n",
        "    self.current[\"energy\"] = energy\n",
        "    self.current[\"indices\"].append(operatorIndex)\n",
        "    self.current[\"total norm\"] = totalNorm\n",
        "\n",
        "    coefficientCopy = copy.deepcopy(self.current[\"coefficients\"])\n",
        "    indicesCopy = copy.deepcopy(self.current[\"indices\"])\n",
        "\n",
        "    self.evolution[\"energies\"].append(energy)\n",
        "    self.evolution[\"energyChange\"].append(energyChange)\n",
        "    self.evolution[\"total norm\"].append(totalNorm)\n",
        "    self.evolution[\"selected gradient\"].append(selectedGradient)\n",
        "    self.evolution[\"indices\"].append(indicesCopy) \n",
        "\n",
        "    self.evolution[\"coefficients\"].append(coefficientCopy)\n",
        "\n",
        "    self.iterationCounter += 1\n",
        "\n",
        "    #assert(self.iterationCounter == len(self.evolution[\"total norm\"]))\n",
        "    #assert(self.iterationCounter == len(self.evolution[\"coefficients\"]))\n",
        "    #assert(self.iterationCounter == len(self.evolution[\"energies\"]))\n",
        "\n",
        "  def close(self,success):\n",
        "    '''\n",
        "    To be called at the end of the run, to close the data structures\n",
        "\n",
        "    Arguments:\n",
        "      success (bool): True if the convergence condition was met, False if not\n",
        "        (the maximum number of iterations was met before that)\n",
        "    '''\n",
        "\n",
        "    self.result = self.current\n",
        "    self.closed = True\n",
        "    self.success = success\n",
        "\n",
        "  def plot(self, plotGradient = True, detailedTitle = True):\n",
        "    '''\n",
        "    Plots the evolution of the energy along the run.\n",
        "\n",
        "    Arguments:\n",
        "      plotGradient (bool): whether the total gradient norm should be plotted \n",
        "        as well.\n",
        "      detailedTitle (bool): whether the title should have include which was the\n",
        "        used backend and the shot number.\n",
        "    '''\n",
        "\n",
        "    iterationNumber = self.iterationCounter\n",
        "    iterationLabels = [iterationLable \n",
        "                      for iterationLable in range(0,self.iterationCounter+1)]\n",
        "\n",
        "\n",
        "    gradientNorms = self.evolution[\"total norm\"]\n",
        "    energies = [self.initialEnergy] + self.evolution[\"energies\"]\n",
        "\n",
        "    fig, ax1 = plt.subplots(figsize=[8,8])\n",
        "    title = \"Qubit Adapt VQE for {}\".format(molecule.description)\n",
        "\n",
        "    if self.backend is None:\n",
        "      backendName = None\n",
        "\n",
        "    elif self.backend.name() == \"statevector_simulator\":\n",
        "      backendName = \"Aer State Vector Simulator\"\n",
        "\n",
        "    elif self.backend.name() == \"qasm_simulator\":\n",
        "      backendName = \"Aer QASM Simulator\"\n",
        "\n",
        "    else:\n",
        "      backendName = self.backend.name()\n",
        "\n",
        "    if detailedTitle:\n",
        "      title += \"\\nr = {}Å\".format(str(bondLength))\n",
        "      if backendName is not None:\n",
        "        title += \", {}\".format(backendName)\n",
        "      if backendName != \"Aer State Vector Simulator\":\n",
        "        title += \", {} shots\".format(self.shots)\n",
        "\n",
        "    plt.title(title)\n",
        "\n",
        "    color1 = 'b'  \n",
        "    ax1.plot(iterationLabels, energies, '--o',color = color1)\n",
        "    ax1.tick_params(axis='y', labelcolor = color1)\n",
        "\n",
        "    # Shade area within chemical accuracy\n",
        "    exactEnergy = molecule.fci_energy\n",
        "    minAccuracy = exactEnergy - chemicalAccuracy\n",
        "    maxAccuracy = exactEnergy + chemicalAccuracy\n",
        "    l = ax1.axhspan(minAccuracy, maxAccuracy, alpha=0.3, color = 'cornflowerblue')\n",
        "\n",
        "    ax1.set_xlabel(\"Iteration Number\")\n",
        "    ax1.set_ylabel(\"Energy (au)\",color = color1)\n",
        "    \n",
        "    plt.xticks(range(1, 1 + self.iterationCounter))\n",
        "\n",
        "    if plotGradient:\n",
        "      color2 = 'r'\n",
        "      ax2 = ax1.twinx()\n",
        "      ax2.plot(iterationLabels[1:], gradientNorms, '--o',color = color2)\n",
        "      ax2.tick_params(axis='y', labelcolor = color2)\n",
        "      ax2.set_ylabel(\"Total Gradient Norm\",color = color2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhKAS__aZKWP"
      },
      "source": [
        "## AdaptVQE Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBSXVmsiZNEz"
      },
      "source": [
        "class AdaptVQE:\n",
        "  '''\n",
        "  Class for running the VQE algorithm.\n",
        "\n",
        "  Methods:\n",
        "    prepare: to be called before the run, in order to prepare the sparse pool\n",
        "      and possibly upload previous data\n",
        "    printSettings: print the algorithm options\n",
        "    computeState: calculate the state with the current ansatz and coefficients\n",
        "    calculateOperatorGradient: calculate the absolute vale of the gradient of \n",
        "      a given operator\n",
        "    selectOperator: select the operator to be added to the ansatz\n",
        "    calculateEnergy: calculate the energy with a certain ansatz and coefficients\n",
        "    callback: store intermediate data from the optimization\n",
        "    optimizeCircuit: optimize a parametrized circuit with a given ansatz\n",
        "    run: run the algorithm\n",
        "  '''\n",
        "\n",
        "  def __init__(self,\n",
        "               pool,\n",
        "               molecule,\n",
        "               qinstance,\n",
        "               referenceDeterminant = None,\n",
        "               verbose = False,\n",
        "               maxIterations = 50,\n",
        "               threshold = 0.1,\n",
        "               shots = 1000):\n",
        "    '''\n",
        "    Initialize class instance\n",
        "\n",
        "    Arguments:\n",
        "      pool (list): operator pool\n",
        "      molecule (openfermion.MolecularData): the molecule we're finding the \n",
        "        ground state of\n",
        "      referenceDeterminant (list): the Slater determinant to be used as reference,\n",
        "        in big endian ordering (|abcd> <-> [a,b,c,d]; [qubit 0, qubit 1,...]).\n",
        "        If none, the Hartree Fock determinant will be used.\n",
        "      verbose (bool): whether to print all the non-zero gradients, or to just\n",
        "        leave out that information\n",
        "      maxIterations (int): maximum allowed number of iterations until forcing\n",
        "        a stop, even if the convergence threshold isn't met\n",
        "      threshold (float): the convergence threshold. When the total gradient norm\n",
        "        is lower than this, the algorithm will stop\n",
        "      backend (Union[None,qiskit.providers.ibmq.IBMQBackend]): the backend to \n",
        "        be used. If none, a simulation will be run using sparse matrices.\n",
        "      shots (int): number of circuit repetitions\n",
        "    '''\n",
        "    self.pool = pool.copy()\n",
        "    self.molecule = molecule\n",
        "    self.verbose = verbose\n",
        "\n",
        "    self.referenceDeterminant = referenceDeterminant\n",
        "    self.electronNumber = molecule.n_electrons\n",
        "    self.hamiltonian = molecule.get_molecular_hamiltonian()\n",
        "    self.qubitNumber = self.hamiltonian.n_qubits\n",
        "    self.sparseHamiltonian = get_sparse_operator(self.hamiltonian,self.qubitNumber)\n",
        "    self.qubitHamiltonian = jordan_wigner(self.hamiltonian)\n",
        "    \n",
        "    dictHamiltonian = convertHamiltonian(self.qubitHamiltonian)\n",
        "    self.aquaObservable = toAquaObservable(dictHamiltonian)\n",
        "  \n",
        "    # Format and group the Hamiltonian, so as to save measurements in the \n",
        "    #circuit simulation by using the same data for Pauli strings that only \n",
        "    #differ by identities\n",
        "    self.formattedHamiltonian = convertHamiltonian(self.qubitHamiltonian)\n",
        "    self.groupedHamiltonian = groupHamiltonian(self.formattedHamiltonian)\n",
        "\n",
        "    self.maxIterations = maxIterations\n",
        "    self.threshold = threshold\n",
        "\n",
        "    self.backend = backend\n",
        "    self.shots = shots\n",
        "\n",
        "    self.sparsePool = []\n",
        "    self.ansatz = []\n",
        "    self.coefficients = []\n",
        "    self.indices = []\n",
        "\n",
        "  def prepare(self,\n",
        "              sparsePool = None,\n",
        "              previousData = None):\n",
        "    '''\n",
        "    Prepare to run the algorithm\n",
        "\n",
        "    Arguments:\n",
        "      sparsePool (list): a sparse version of the pool, to avoid reobtaining \n",
        "        sparse versions of the operators if they've already been obtained\n",
        "      previousData (AdaptData): data from a previous run, that will be continued\n",
        "      \n",
        "    '''\n",
        "    # If they weren't provided, obtain sparse versions of the operators to \n",
        "    #avoid constantly recalculating them\n",
        "    if sparsePool is None:\n",
        "      print(\"Sparse version of the pool was not provided. Obtaining it...\")\n",
        "\n",
        "      for operator in self.pool:\n",
        "        self.sparsePool.append(get_sparse_operator(operator,self.qubitNumber))\n",
        "\n",
        "    else:\n",
        "      self.sparsePool = sparsePool\n",
        "\n",
        "    print(\"Initializing data structures...\")\n",
        "\n",
        "    if self.referenceDeterminant is None:\n",
        "      # Set the Hartree Fock state as reference\n",
        "      self.referenceDeterminant = getHartreeFockKet(self.electronNumber,self.qubitNumber)\n",
        "\n",
        "    print(\"\\nAdapt VQE prepared with the following settings:\")\n",
        "    self.printSettings()\n",
        "    self.referenceState = fromKettoVector(self.referenceDeterminant)\n",
        "\n",
        "    self.sparseReferenceState = scipy.sparse.csc_matrix(\n",
        "        self.referenceState,dtype=complex\n",
        "        ).transpose()\n",
        "\n",
        "    # Calculate energy of the reference state.\n",
        "    initialEnergy = self.calculateEnergy([],[])\n",
        "\n",
        "    # Initialize insance of AdaptData class that will store the data from the \n",
        "    #run\n",
        "    self.data = AdaptData(initialEnergy,\n",
        "                          self.pool,\n",
        "                          self.sparsePool,\n",
        "                          self.referenceDeterminant,\n",
        "                          self.backend,\n",
        "                          self.shots,\n",
        "                          previousData)\n",
        "        \n",
        "    if self.data.current[\"state\"] is None:\n",
        "      # There's no previous data; initialize state at reference\n",
        "      self.data.current[\"state\"] = self.sparseReferenceState\n",
        "    \n",
        "  def printSettings(self):\n",
        "      '''\n",
        "      Prints the options that were chosen for the Adapt VQE run.\n",
        "      '''\n",
        "\n",
        "      print(\"> Convergence threshold (gradient norm): \", self.threshold)\n",
        "      print(\"> Maximum number of iterations: \", self.maxIterations)\n",
        "      print(\"> Backend: \",self.backend)\n",
        "      if self.backend is None:\n",
        "        print(\"(using sparse matrices)\")\n",
        "      elif backend.name() != \"statevector_simulator\":\n",
        "        print(\"(Shot number: {})\".format(self.shots))\n",
        "\n",
        "  def computeState(self):\n",
        "    '''\n",
        "      Calculates the state with the current ansatz and coefficients.\n",
        "\n",
        "      Returns:\n",
        "        state (scipy.sparse.csc_matrix): the state\n",
        "    '''\n",
        "\n",
        "    # Since the coefficients are reoptimized every iteration, the state has to \n",
        "    #be built from the reference state each time.\n",
        "\n",
        "    # Initialize the state vector with the reference state.\n",
        "    state = self.sparseReferenceState\n",
        "\n",
        "    # Apply the ansatz operators one by one to obtain the state as optimized\n",
        "    #by the last iteration\n",
        "    for (i,operatorIndex) in enumerate(self.data.current[\"indices\"]):\n",
        "\n",
        "      # Obtain the exponentiated pool operator, multiplied by the respective\n",
        "      #variational parameter (as optimized by the last iteration)\n",
        "      coefficient = self.data.current[\"coefficients\"][i]\n",
        "      sparseOperator = self.sparsePool[operatorIndex].multiply(coefficient)\n",
        "      expOperator = scipy.sparse.linalg.expm(sparseOperator)\n",
        "\n",
        "      # Act on the state with the operator \n",
        "      state = expOperator * state\n",
        "\n",
        "    return state\n",
        "\n",
        "  def calculateOperatorGradient(self,operatorIndex):\n",
        "    '''\n",
        "      Calculates the gradient of a given operator in the current state.\n",
        "      Uses dexp(c*A)/dc = <psi|[H,A]|psi> = 2 * real(<psi|HA|psi>).\n",
        "      This is the gradient calculated at c = 0, which will be the initial value \n",
        "      of the coefficient in the optimization.\n",
        "\n",
        "      Arguments:\n",
        "        operatorIndex (int): the index that labels this operator\n",
        "      \n",
        "      Returns:\n",
        "        gradient (float): the norm of the gradient of this operator in the \n",
        "          current state\n",
        "    '''\n",
        "\n",
        "    sparseOperator = self.sparsePool[operatorIndex]\n",
        "    currentState = self.data.current[\"state\"]\n",
        "\n",
        "    testState = sparseOperator * currentState\n",
        "    bra = currentState.transpose().conj()\n",
        "\n",
        "    gradient = 2 * (np.abs(bra * self.sparseHamiltonian * testState)[0,0].real)\n",
        "\n",
        "    return gradient\n",
        "  \n",
        "  def selectOperator(self):\n",
        "    ''' \n",
        "    Choose the next operator to be added to the ansatz, using as criterion\n",
        "    that the one with the maximum gradient norm is the selected one.\n",
        "\n",
        "    Returns:\n",
        "      selectedIndex (int): the index that labels the selected operator\n",
        "      selectedGradient (float): the norm of the gradient of that operator\n",
        "      totalNorm (float): the total gradient norm\n",
        "    '''\n",
        "\n",
        "    selectedGradient = 0\n",
        "    selectedIndex = None\n",
        "    totalNorm = 0\n",
        "\n",
        "    print(\"Calculating gradients and selecting the next operator...\")\n",
        "    \n",
        "    if self.verbose:\n",
        "      print(\"\\nNon-Zero Gradients (calculated, tolerance E-5):\")\n",
        "\n",
        "    for operatorIndex in range(len(self.pool)):\n",
        "\n",
        "      gradient = self.calculateOperatorGradient(operatorIndex)\n",
        "\n",
        "      totalNorm += gradient**2\n",
        "\n",
        "      if self.verbose:\n",
        "        if gradient > 10**-5:\n",
        "          print(\"Operator {}: {}\".format(operatorIndex,gradient))\n",
        "\n",
        "      if gradient > selectedGradient:\n",
        "        selectedIndex = operatorIndex\n",
        "        selectedGradient = gradient\n",
        "\n",
        "    totalNorm = np.sqrt(totalNorm)\n",
        "    \n",
        "    print(\"Total gradient norm: {}\".format(totalNorm))\n",
        "\n",
        "    return selectedIndex, selectedGradient, totalNorm\n",
        "\n",
        "  def calculateEnergy(self,coefficients,indices):\n",
        "    '''\n",
        "    Calculates the energy in a specified state.\n",
        "\n",
        "    Arguments:\n",
        "      coefficients (list): coefficients of the ansatz indices\n",
        "      indices (list): indices that specify which operators are in the ansatz.\n",
        "\n",
        "    Returns:\n",
        "      energy (float): the energy in this state.\n",
        "    '''\n",
        "\n",
        "    assert(len(coefficients) == len(indices))\n",
        "    \n",
        "    ket = self.sparseReferenceState\n",
        "\n",
        "    # Apply e ** (coefficient * operator) to the state (ket) for each operator in \n",
        "    #the ansatz, following the order of the list\n",
        "    for (coefficient,operatorIndex) in zip(coefficients,indices):\n",
        "\n",
        "      # Multiply the operator by the respective coefficient\n",
        "      sparseOperator = coefficient * self.sparsePool[operatorIndex]\n",
        "\n",
        "      # Exponentiate the operator and update ket to represent the state after\n",
        "      #this operator has been applied\n",
        "      expOperator = scipy.sparse.linalg.expm(sparseOperator)\n",
        "      ket = expOperator * ket\n",
        "\n",
        "    # Get the corresponding bra and calculate the energy: |<bra| H |ket>|\n",
        "    bra = ket.transpose().conj()\n",
        "    energy = (bra * self.sparseHamiltonian * ket)[0,0].real\n",
        "\n",
        "    return energy\n",
        "\n",
        "  def callback(self, nfev, parameters, energy, stddev):\n",
        "    '''\n",
        "    Function to gather intermediate data from the optimization. Fills the \n",
        "    instance variable optEvolution with the data from each step in the \n",
        "    optimization process.\n",
        "    '''\n",
        "\n",
        "    self.optEvolution['nfev'].append(nfev)\n",
        "    self.optEvolution['parameters'].append(parameters.copy())\n",
        "    self.optEvolution['energy'].append(energy)\n",
        "    self.optEvolution['stddev'].append(stddev)\n",
        "        \n",
        "  def optimizeCircuit(self,initialCoefficients,indices):\n",
        "    '''\n",
        "    Optimizes a certain ansatz, filling the instance variable optEvolution \n",
        "      with the optimization data.\n",
        "\n",
        "    Arguments:\n",
        "      initialCoefficients (list): the coefficients that define the starting \n",
        "        point.\n",
        "      indices (list): the indices that identify the ansatz to be optimized\n",
        "    '''\n",
        "    \n",
        "    # Initialize instance variable, that will be filled with the data from this \n",
        "    #optimization. The data from the previous one will be deleted.\n",
        "    self.optEvolution = {\n",
        "        'nfev': [],\n",
        "        'parameters': [],\n",
        "        'energy': [],\n",
        "        'stddev': []\n",
        "    }\n",
        "\n",
        "    # Initialize parameter vector and circuit in Qiskit\n",
        "    parameters = ParameterVector(\"Params\",len(indices))\n",
        "    ansatz = QuantumCircuit(self.qubitNumber)\n",
        "\n",
        "    # Apply gates that prepare the reference determinant\n",
        "    for i,state in enumerate(self.referenceDeterminant):\n",
        "      if state == 1:\n",
        "        ansatz.x(self.qubitNumber - 1 - i)\n",
        "\n",
        "    # Apply gates corresponding to each of the ansatz operators.\n",
        "    for i, op in enumerate(indices):\n",
        "      pauliToCircuit(self.pool[op],\n",
        "                      parameters[i],\n",
        "                      ansatz,\n",
        "                      self.qubitNumber)\n",
        "\n",
        "    vqe = VQE(self.aquaObservable,\n",
        "              var_form = ansatz, \n",
        "              optimizer = COBYLA(rhobeg = 0.1), \n",
        "              quantum_instance = qinstance,\n",
        "              callback = self.callback,\n",
        "              initial_point = initialCoefficients)\n",
        "\n",
        "    result = vqe.run()\n",
        "\n",
        "  def run(self):\n",
        "    '''\n",
        "    Run the full Adapt VQE algorithm, until either the convergence condition is\n",
        "      met or the maximum number of iterations is reached.\n",
        "    '''\n",
        "\n",
        "    while self.data.iterationCounter < self.maxIterations:\n",
        "\n",
        "      print(\"\\n*** Adapt Iteration {} ***\\n\".format\n",
        "            (self.data.iterationCounter + 1))\n",
        "\n",
        "      maxIndex, maxGradient, totalNorm = self.selectOperator()\n",
        "      maxOperator = self.pool[maxIndex]\n",
        "\n",
        "      if totalNorm < self.threshold:\n",
        "\n",
        "        print(\"\\nConvergence condition achieved!\\n\")\n",
        "        self.data.close(success = True)\n",
        "\n",
        "        if self.data.result[\"energy\"] is None:\n",
        "          print(\"The chosen threshold was too large;\" \n",
        "                \" no iterations were completed.\")\n",
        "          return\n",
        "\n",
        "        print(\"Final Energy:\", self.data.result[\"energy\"])\n",
        "        error = self.data.result[\"energy\"] - molecule.fci_energy\n",
        "        print(\"Error:\",error)\n",
        "        print(\"(in % of chemical accuracy: {:.3f}%)\\n\".format\n",
        "              (error/chemicalAccuracy*100))\n",
        "        \n",
        "        print(\"Ansatz Indices:\", self.data.result[\"indices\"])\n",
        "        print(\"Coefficients:\", self.data.result[\"coefficients\"])\n",
        "\n",
        "        return\n",
        "\n",
        "      print(\"Selected: {}, index {}\".format(self.pool[maxIndex],maxIndex))\n",
        "\n",
        "      print(\"(gradient: {})\".format(maxGradient))\n",
        "\n",
        "      newIndices = self.data.current[\"indices\"].copy()\n",
        "      newIndices.append(maxIndex)\n",
        "\n",
        "      # Initialize the coefficient of the operator that will be newly added at 0\n",
        "      newCoefficients = copy.deepcopy(self.data.current[\"coefficients\"])\n",
        "      newCoefficients.append(0)\n",
        "\n",
        "      print(\"\\nOptimizing energy with indices {}...\".format\n",
        "            (self.data.current[\"indices\"] + [maxIndex]))\n",
        "      indices = self.data.current[\"indices\"] + [maxIndex]\n",
        "\n",
        "      if self.backend is None:\n",
        "        # Use sparse matrices\n",
        "        opt_result = scipy.optimize.minimize(self.calculateEnergy, \n",
        "                                            newCoefficients,\n",
        "                                            indices,\n",
        "                                            method = \"COBYLA\",\n",
        "                                            #tol = 10**-4,\n",
        "                                            options = {'rhobeg': 0.1,\n",
        "                                                        'disp': True})\n",
        "        optimizedCoefficients = list(opt_result.x)\n",
        "        nfev = opt_result.nfev\n",
        "\n",
        "      else:\n",
        "        # Optimize the ansatz in Qiskit\n",
        "        self.optimizeCircuit(newCoefficients,indices)\n",
        "\n",
        "        evolution = self.optEvolution\n",
        "        optimizedCoefficients = list(self.optEvolution[\"parameters\"][-1])\n",
        "        nfev = self.optEvolution[\"nfev\"][-1]\n",
        "        \n",
        "      optimizedEnergy = self.calculateEnergy(optimizedCoefficients,\n",
        "                                             indices)\n",
        "      \n",
        "      print(\"Number of function evaluations:\",nfev)\n",
        "      print(\"Optimized energy: \",optimizedEnergy)\n",
        "      print(\"Optimized coefficients: \", optimizedCoefficients)\n",
        "        \n",
        "      self.data.processIteration(maxIndex,\n",
        "                             maxOperator,\n",
        "                             optimizedEnergy,\n",
        "                             totalNorm,\n",
        "                             maxGradient,\n",
        "                             optimizedCoefficients)\n",
        "      \n",
        "      print(\"Current ansatz:\", self.data.current[\"ansatz\"])\n",
        "      \n",
        "      # Update current state\n",
        "      newState = self.computeState()\n",
        "      self.data.current[\"state\"] = newState\n",
        "      \n",
        "      print(\"\\nEnergy Changes Associated with the Indices:\",\n",
        "            self.data.evolution[\"energyChange\"])\n",
        "      print(\"Performances Associated with the Indices: \",\n",
        "            self.data.current[\"ansatz performances\"])\n",
        "    \n",
        "    print(\"\\nThe maximum number of iterations ({}) was hit before the\"\n",
        "    \" convergence criterion was satisfied.\\n\"\n",
        "    \"(current gradient norm is {} > {})\"\n",
        "    .format(self.maxIterations,self.data.current[\"total norm\"],self.threshold))\n",
        "    self.data.close(success = False)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Pit4sRCufDb"
      },
      "source": [
        "## Noise Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJbRUc78g1Cn"
      },
      "source": [
        "### Backend noise models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xlu587inugWT"
      },
      "source": [
        "# Noise model from ibmq_belem\n",
        "\n",
        "device_backend = FakeBelem()\n",
        "device = QasmSimulator.from_backend(device_backend)\n",
        "coupling_map = device.configuration().coupling_map\n",
        "\n",
        "belemNM = NoiseModel.from_backend(device)\n",
        "basis_gates = belemNM.basis_gates"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9jJ0ieVMaZf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f88be636-b7b8-429d-c8ea-4822b2f92c95"
      },
      "source": [
        "config = device_backend.configuration()\n",
        "props = device_backend.properties()\n",
        "gateErrors = basic_device_gate_errors(props,thermal_relaxation = False)\n",
        "\n",
        "import pprint\n",
        "pprint.pprint(gateErrors)\n",
        "print(gate_error_values(props))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('id',\n",
            "  [0],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'y', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'z', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'id', 'qubits': [0]}], 0.9996538241850126)])),\n",
            " ('id',\n",
            "  [1],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'y', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'z', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'id', 'qubits': [0]}], 0.9993833673722124)])),\n",
            " ('id',\n",
            "  [2],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'y', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'z', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'id', 'qubits': [0]}], 0.9995545487441548)])),\n",
            " ('id',\n",
            "  [3],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'y', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'z', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'id', 'qubits': [0]}], 0.9989904447350599)])),\n",
            " ('id',\n",
            "  [4],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'y', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'z', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'id', 'qubits': [0]}], 0.9995033041662921)])),\n",
            " ('sx',\n",
            "  [0],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'y', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'z', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'id', 'qubits': [0]}], 0.9996538241850126)])),\n",
            " ('sx',\n",
            "  [1],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'y', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'z', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'id', 'qubits': [0]}], 0.9993833673722124)])),\n",
            " ('sx',\n",
            "  [2],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'y', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'z', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'id', 'qubits': [0]}], 0.9995545487441548)])),\n",
            " ('sx',\n",
            "  [3],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'y', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'z', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'id', 'qubits': [0]}], 0.9989904447350599)])),\n",
            " ('sx',\n",
            "  [4],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'y', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'z', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'id', 'qubits': [0]}], 0.9995033041662921)])),\n",
            " ('x',\n",
            "  [0],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'y', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'z', 'qubits': [0]}], 0.00011539193832914837), ([{'name': 'id', 'qubits': [0]}], 0.9996538241850126)])),\n",
            " ('x',\n",
            "  [1],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'y', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'z', 'qubits': [0]}], 0.0002055442092625086), ([{'name': 'id', 'qubits': [0]}], 0.9993833673722124)])),\n",
            " ('x',\n",
            "  [2],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'y', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'z', 'qubits': [0]}], 0.0001484837519483761), ([{'name': 'id', 'qubits': [0]}], 0.9995545487441548)])),\n",
            " ('x',\n",
            "  [3],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'y', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'z', 'qubits': [0]}], 0.0003365184216466817), ([{'name': 'id', 'qubits': [0]}], 0.9989904447350599)])),\n",
            " ('x',\n",
            "  [4],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'y', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'z', 'qubits': [0]}], 0.00016556527790263407), ([{'name': 'id', 'qubits': [0]}], 0.9995033041662921)])),\n",
            " ('cx',\n",
            "  [4, 3],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [0]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [0]}], 0.00122906312732457), ([{'name': 'x', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'id', 'qubits': [0]}], 0.9815640530901315)])),\n",
            " ('cx',\n",
            "  [3, 4],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [0]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [0]}], 0.00122906312732457), ([{'name': 'x', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.00122906312732457), ([{'name': 'id', 'qubits': [0]}], 0.9815640530901315)])),\n",
            " ('cx',\n",
            "  [3, 1],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [0]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [0]}], 0.0011619552141440906), ([{'name': 'x', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'id', 'qubits': [0]}], 0.9825706717878386)])),\n",
            " ('cx',\n",
            "  [1, 3],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [0]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [0]}], 0.0011619552141440906), ([{'name': 'x', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0011619552141440906), ([{'name': 'id', 'qubits': [0]}], 0.9825706717878386)])),\n",
            " ('cx',\n",
            "  [2, 1],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [0]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [0]}], 0.000891121324534061), ([{'name': 'x', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'id', 'qubits': [0]}], 0.9866331801319891)])),\n",
            " ('cx',\n",
            "  [1, 2],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [0]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [0]}], 0.000891121324534061), ([{'name': 'x', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.000891121324534061), ([{'name': 'id', 'qubits': [0]}], 0.9866331801319891)])),\n",
            " ('cx',\n",
            "  [1, 0],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [0]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [0]}], 0.0013798737995859799), ([{'name': 'x', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'id', 'qubits': [0]}], 0.9793018930062103)])),\n",
            " ('cx',\n",
            "  [0, 1],\n",
            "  QuantumError([([{'name': 'x', 'qubits': [0]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [0]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [0]}], 0.0013798737995859799), ([{'name': 'x', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'x', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [0]}, {'name': 'x', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'x', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [0]}, {'name': 'y', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'x', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'y', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'z', 'qubits': [0]}, {'name': 'z', 'qubits': [1]}], 0.0013798737995859799), ([{'name': 'id', 'qubits': [0]}], 0.9793018930062103)]))]\n",
            "[('id', [0], 0.00023078387665829674), ('id', [1], 0.0004110884185250172), ('id', [2], 0.0002969675038967522), ('id', [3], 0.0006730368432933634), ('id', [4], 0.00033113055580526814), ('rz', [0], 0), ('rz', [1], 0), ('rz', [2], 0), ('rz', [3], 0), ('rz', [4], 0), ('sx', [0], 0.00023078387665829674), ('sx', [1], 0.0004110884185250172), ('sx', [2], 0.0002969675038967522), ('sx', [3], 0.0006730368432933634), ('sx', [4], 0.00033113055580526814), ('x', [0], 0.00023078387665829674), ('x', [1], 0.0004110884185250172), ('x', [2], 0.0002969675038967522), ('x', [3], 0.0006730368432933634), ('x', [4], 0.00033113055580526814), ('cx', [4, 3], 0.01474875752789484), ('cx', [3, 4], 0.01474875752789484), ('cx', [3, 1], 0.013943462569729087), ('cx', [1, 3], 0.013943462569729087), ('cx', [2, 1], 0.010693455894408732), ('cx', [1, 2], 0.010693455894408732), ('cx', [1, 0], 0.016558485595031758), ('cx', [0, 1], 0.016558485595031758), ('reset', [0], None), ('reset', [1], None), ('reset', [2], None), ('reset', [3], None), ('reset', [4], None)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ieao3UhDgwjW"
      },
      "source": [
        "### Isolate noise from different operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Pb8IADVukJy",
        "outputId": "91c3cd01-7e4f-4e54-e673-577e0739a5ff"
      },
      "source": [
        "# Isolate noise (from the backend model) associated with each type of gates\n",
        "\n",
        "noiseDict = belemNM.to_dict()\n",
        "\n",
        "measNoiseDict = {}\n",
        "resetNoiseDict = {}\n",
        "idNoiseDict = {}\n",
        "sxNoiseDict = {}\n",
        "xNoiseDict = {}\n",
        "cxNoiseDict = {}\n",
        "\n",
        "measNoiseDict[\"errors\"] = []\n",
        "resetNoiseDict[\"errors\"] = []\n",
        "idNoiseDict[\"errors\"] = []\n",
        "sxNoiseDict[\"errors\"] = []\n",
        "xNoiseDict[\"errors\"] = []\n",
        "cxNoiseDict[\"errors\"] = []\n",
        "\n",
        "cxMeasNoiseDict = {}\n",
        "cxMeasNoiseDict[\"errors\"] = []\n",
        "\n",
        "# Add to the new noise model the desired errors from the ibmq_belem noise model.\n",
        "for error in noiseDict[\"errors\"]:\n",
        "  #print(error[\"type\"],error[\"operations\"])\n",
        "  # error[\"operations\"][0] is one of [\"measure\",\"reset\",\"id\",\"sx\",\"x\",\"cx\"]\n",
        "\n",
        "  if error[\"operations\"][0] == \"measure\":\n",
        "    measNoiseDict[\"errors\"].append(error)\n",
        "  if error[\"operations\"][0] == \"reset\":\n",
        "    resetNoiseDict[\"errors\"].append(error)\n",
        "  if error[\"operations\"][0] == \"id\":\n",
        "    idNoiseDict[\"errors\"].append(error)\n",
        "  if error[\"operations\"][0] == \"sx\":\n",
        "    sxNoiseDict[\"errors\"].append(error)\n",
        "  if error[\"operations\"][0] == \"x\":\n",
        "    xNoiseDict[\"errors\"].append(error)\n",
        "  if error[\"operations\"][0] == \"cx\":\n",
        "    cxNoiseDict[\"errors\"].append(error)\n",
        "\n",
        "  if error[\"operations\"][0] in [\"cx\",\"measure\"]:\n",
        "    cxMeasNoiseDict[\"errors\"].append(error)\n",
        "\n",
        "measNoiseModel = NoiseModel(basis_gates=belemNM.basis_gates)\n",
        "resetNoiseModel = NoiseModel(basis_gates=belemNM.basis_gates)\n",
        "idNoiseModel = NoiseModel(basis_gates=belemNM.basis_gates)\n",
        "sxNoiseModel = NoiseModel(basis_gates=belemNM.basis_gates)\n",
        "xNoiseModel = NoiseModel(basis_gates=belemNM.basis_gates)\n",
        "cxNoiseModel = NoiseModel(basis_gates=belemNM.basis_gates)\n",
        "\n",
        "measNoiseModel = measNoiseModel.from_dict(measNoiseDict)\n",
        "resetNoiseModel = resetNoiseModel.from_dict(resetNoiseDict)\n",
        "idNoiseModel = idNoiseModel.from_dict(idNoiseDict)\n",
        "sxNoiseModel = sxNoiseModel.from_dict(sxNoiseDict)\n",
        "xNoiseModel = xNoiseModel.from_dict(xNoiseDict)\n",
        "cxNoiseModel = cxNoiseModel.from_dict(cxNoiseDict)\n",
        "\n",
        "cxMeasNoiseModel = NoiseModel(basis_gates=belemNM.basis_gates)\n",
        "cxMeasNoiseModel = cxMeasNoiseModel.from_dict(cxMeasNoiseDict)\n",
        "\n",
        "print(measNoiseModel)\n",
        "print(resetNoiseModel)\n",
        "print(idNoiseModel)\n",
        "print(sxNoiseModel)\n",
        "print(xNoiseModel)\n",
        "print(cxNoiseModel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NoiseModel:\n",
            "  Basis gates: ['cx', 'id', 'rz', 'sx']\n",
            "  Instructions with noise: ['measure']\n",
            "  Qubits with noise: [0, 1, 2, 3, 4]\n",
            "  Specific qubit errors: [('measure', [0]), ('measure', [1]), ('measure', [2]), ('measure', [3]), ('measure', [4])]\n",
            "NoiseModel:\n",
            "  Basis gates: ['cx', 'id', 'rz', 'sx']\n",
            "  Instructions with noise: ['reset']\n",
            "  Qubits with noise: [0, 1, 2, 3, 4]\n",
            "  Specific qubit errors: [('reset', [0]), ('reset', [1]), ('reset', [2]), ('reset', [3]), ('reset', [4])]\n",
            "NoiseModel:\n",
            "  Basis gates: ['cx', 'id', 'rz', 'sx']\n",
            "  Instructions with noise: ['id']\n",
            "  Qubits with noise: [0, 1, 2, 3, 4]\n",
            "  Specific qubit errors: [('id', [0]), ('id', [1]), ('id', [2]), ('id', [3]), ('id', [4])]\n",
            "NoiseModel:\n",
            "  Basis gates: ['cx', 'id', 'rz', 'sx']\n",
            "  Instructions with noise: ['sx']\n",
            "  Qubits with noise: [0, 1, 2, 3, 4]\n",
            "  Specific qubit errors: [('sx', [0]), ('sx', [1]), ('sx', [2]), ('sx', [3]), ('sx', [4])]\n",
            "NoiseModel:\n",
            "  Basis gates: ['cx', 'id', 'rz', 'sx', 'x']\n",
            "  Instructions with noise: ['x']\n",
            "  Qubits with noise: [0, 1, 2, 3, 4]\n",
            "  Specific qubit errors: [('x', [0]), ('x', [1]), ('x', [2]), ('x', [3]), ('x', [4])]\n",
            "NoiseModel:\n",
            "  Basis gates: ['cx', 'id', 'rz', 'sx']\n",
            "  Instructions with noise: ['cx']\n",
            "  Qubits with noise: [0, 1, 2, 3, 4]\n",
            "  Specific qubit errors: [('cx', [4, 3]), ('cx', [3, 4]), ('cx', [3, 1]), ('cx', [1, 3]), ('cx', [2, 1]), ('cx', [1, 2]), ('cx', [1, 0]), ('cx', [0, 1])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhlCsZ9ngucm"
      },
      "source": [
        "### Customized Noise Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c1_WaNdLyeV0"
      },
      "source": [
        "def createNoiseModel(t1,t2,measTime,cxTime,m0p1,m1p0):\n",
        "  '''\n",
        "  Creates an approximate noise model, with:\n",
        "  - Thermal relaxation on CX gates / measurements\n",
        "  - Readout error\n",
        "\n",
        "  Arguments:\n",
        "    t1 (float): relaxation time (average)\n",
        "    t2 (float): dephasing time (average)\n",
        "    cxTime (float): CNOT gate time (average)\n",
        "    m0p1 (float): prepare |1> measure |0> probability (average)\n",
        "    m1p0 (float): prepare |0> measure |1> probability (average)\n",
        "\n",
        "  Returns:\n",
        "    noiseModel (qiskit.providers.aer.noise.noise_model.NoiseModel): the noise\n",
        "      model. Assumes average behaviour for all errors\n",
        "  '''\n",
        "  noiseModel = NoiseModel()\n",
        "\n",
        "  # Thermal relaxation\n",
        "  measError = thermal_relaxation_error(t1, t2, measTime)\n",
        "  cxTError = thermal_relaxation_error(t1, t2, cxTime)\n",
        "  cxTError = cxTError.tensor(cxTError)\n",
        "\n",
        "  for i in range(qubitNumber):\n",
        "\n",
        "    # Add thermal relaxation on each qubit measurement\n",
        "    noiseModel.add_quantum_error(measError,\"measure\",[i])\n",
        "\n",
        "    # Add thermal relaxation + gate error on each possible pair of CNOTs\n",
        "    for j in range(qubitNumber):\n",
        "      if j != i:\n",
        "        noiseModel.add_quantum_error(cxTError,\"cx\",[i,j])\n",
        "\n",
        "  # Readout Error\n",
        "  readoutErrorMatrix = [[1 - m1p0, m1p0],[m0p1, 1 - m0p1]]\n",
        "  readoutError = ReadoutError(readoutErrorMatrix)\n",
        "\n",
        "  for i in range(qubitNumber):\n",
        "    # Adde radout error on each qubit\n",
        "    noiseModel.add_readout_error(readoutError,[i])\n",
        "\n",
        "  return noiseModel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-q7nmh9ggAk"
      },
      "source": [
        "### Measuring the purity at the end of the ansatz"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7P6djtIneb4L"
      },
      "source": [
        "def getFinalState(circuit,device_backend):\n",
        "  '''\n",
        "  Obtain density matrix at the end of the ansatz circuit using a noise model. \n",
        "\n",
        "  Arguments:\n",
        "    circuit (qiskit.QuantumCircuit): the circuit\n",
        "    device_backend: noise data from qiskit.test.mock\n",
        "\n",
        "  Returns:\n",
        "    dmatrix (numpy.ndarray): density matrix representing the final state\n",
        "  '''\n",
        "\n",
        "  device = QasmSimulator.from_backend(device_backend)\n",
        "  coupling_map = device.configuration().coupling_map\n",
        "\n",
        "  noiseModel = NoiseModel.from_backend(device)\n",
        "  basis_gates = noiseModel.basis_gates\n",
        "\n",
        "  tcirc = transpile(circuit, device_backend)\n",
        "  tcirc.save_density_matrix()\n",
        "\n",
        "  result = execute(tcirc, Aer.get_backend('qasm_simulator'),\n",
        "                  coupling_map=coupling_map,\n",
        "                  basis_gates=basis_gates,\n",
        "                  noise_model=noiseModel).result()\n",
        "                  \n",
        "  dmatrix = (result.data(0))['density_matrix']\n",
        "  \n",
        "  return dmatrix\n",
        "\n",
        "def getIdealState(circuit,device_backend):\n",
        "  '''\n",
        "  Obtain density matrix at the end of the ansatz circuit assuming a noise-free\n",
        "  scenario.\n",
        "\n",
        "  Arguments:\n",
        "    circuit (qiskit.QuantumCircuit): the circuit\n",
        "    device_backend: noise data from qiskit.test.mock. Will be used for \n",
        "      transpilation,...\n",
        "\n",
        "  Returns:\n",
        "    dmatrix (numpy.ndarray): density matrix representing the final state\n",
        "  '''\n",
        "\n",
        "  device = QasmSimulator.from_backend(device_backend)\n",
        "  coupling_map = device.configuration().coupling_map\n",
        "\n",
        "  noiseModel = NoiseModel.from_backend(device)\n",
        "  basis_gates = noiseModel.basis_gates\n",
        "\n",
        "  tcirc = transpile(circuit, device_backend)\n",
        "  tcirc.save_density_matrix()\n",
        "\n",
        "  result = execute(tcirc, Aer.get_backend('statevector_simulator'),\n",
        "                  coupling_map=coupling_map,\n",
        "                  basis_gates=basis_gates,\n",
        "                  noise_model=noiseModel).result()\n",
        "                  \n",
        "  dmatrix = (result.data(0))['density_matrix']\n",
        "  \n",
        "  return dmatrix\n",
        "\n",
        "def getPurity(dmatrix):\n",
        "  '''\n",
        "  Obtain the purity of a state represented by a density matrix.\n",
        "\n",
        "  Arguments:\n",
        "    dmatrix (numpy.ndarray): density matrix representing the state\n",
        "\n",
        "  Returns:\n",
        "    purity (int): the purity of the state \n",
        "  '''\n",
        "\n",
        "  statePurity = purity(dmatrix).real\n",
        "\n",
        "  return statePurity\n",
        "\n",
        "def getFidelity(dmatrix1,dmatrix2):\n",
        "  '''\n",
        "  Obtain the fidelity between two states.\n",
        "\n",
        "  Arguments:\n",
        "    dmatrix1 (numpy.ndarray): density matrix representing state 1\n",
        "    dmatrix2 (numpy.ndarray): density matrix representing state 2\n",
        "\n",
        "  Returns:\n",
        "    fidelity (int): the fidelity between the two states\n",
        "  '''\n",
        "  dimension, _ = dmatrix1.shape\n",
        "\n",
        "  dmatrix1Root = scipy.linalg.sqrtm(dmatrix1)\n",
        "\n",
        "  mat = np.matmul(np.matmul(dmatrix1Root, dmatrix2),dmatrix1Root)\n",
        "  matRoot =  scipy.linalg.sqrtm(mat)\n",
        "\n",
        "  fidelity = np.real((np.trace(matRoot))**2)\n",
        "\n",
        "  return fidelity"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LK6IbMLaNarC",
        "outputId": "3805b272-624e-4180-dc13-ac3d7b5e8783"
      },
      "source": [
        "# Obtain purity at the end of the ansatz circuit using a noise model. \n",
        "#IBMQ Belem noise model here\n",
        "# Test for a single operator\n",
        "\n",
        "# Choose the operator\n",
        "op = qubitPool[10]\n",
        "\n",
        "circ = QuantumCircuit(qubitNumber)\n",
        "pauliToCircuit(op,0,circ,qubitNumber)\n",
        "\n",
        "idealState = getIdealState(circ,FakeBelem())\n",
        "dimension, _ = idealState.shape\n",
        "fullyMixed =  1/dimension * np.identity(dimension)\n",
        "\n",
        "# Number of tests\n",
        "tests = 10\n",
        "\n",
        "purities = []\n",
        "fidelitiesIdeal = []\n",
        "fidelitiesFM = []\n",
        "\n",
        "for _ in range(tests):\n",
        "\n",
        "  finalState = getFinalState(circ,FakeBelem())\n",
        "  finalPurity = getPurity(finalState)\n",
        "  fidelityIdeal = getFidelity(finalState,idealState)\n",
        "  fidelityFM = getFidelity(finalState,fullyMixed)\n",
        "\n",
        "  purities.append(finalPurity)\n",
        "  fidelitiesIdeal.append(fidelityIdeal)\n",
        "  fidelitiesFM.append(fidelityFM)\n",
        "\n",
        "avPurity = np.average(purities)\n",
        "avFidelityFM = np.average(fidelitiesFM)\n",
        "avFidelityIdeal = np.average(fidelitiesIdeal)\n",
        "\n",
        "print(\"Purity of the fully mixed state: \", 1/2**qubitNumber)\n",
        "print(\"Purity at the end of the UCCSD circuit:\", avPurity)\n",
        "print(\"Fidelity with the ideal state: \", avFidelityIdeal)\n",
        "print(\"Fidelity with the fully mixed state: \", avFidelityFM)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Purity of the fully mixed state:  0.0625\n",
            "Purity at the end of the UCCSD circuit: 0.8193062474460507\n",
            "Fidelity with the ideal state:  0.9429338046954573\n",
            "Fidelity with the fully mixed state:  0.1001725751230291\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JvZEJ3zbZZze"
      },
      "source": [
        "## Running the Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPjLQ6PP00-T"
      },
      "source": [
        "threshold = 0.15\n",
        "\n",
        "backend = provider.get_backend('ibmq_belem')\n",
        "backend = Aer.get_backend('qasm_simulator')\n",
        "\n",
        "maxIterations = 1\n",
        "\n",
        "shots = 2**17"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Knzd6VDQetoL",
        "outputId": "045e3925-ef7a-4c3f-d9e5-273e904cc60b"
      },
      "source": [
        "qinstance = QuantumInstance(backend, \n",
        "                            shots = shots,\n",
        "                            coupling_map=coupling_map,\n",
        "                            basis_gates=basis_gates,\n",
        "                            noise_model=belemNM)\n",
        "\n",
        "myAdapt = AdaptVQE(qubitPool,\n",
        "                   molecule,\n",
        "                   qinstance = qinstance,\n",
        "                   verbose = True,\n",
        "                   threshold = threshold,\n",
        "                   maxIterations = maxIterations,\n",
        "                   shots = shots,\n",
        "                   referenceDeterminant = None\n",
        "                   )\n",
        "myAdapt.prepare()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sparse version of the pool was not provided. Obtaining it...\n",
            "Initializing data structures...\n",
            "\n",
            "Adapt VQE prepared with the following settings:\n",
            "> Convergence treshold (gradient norm):  0.15\n",
            "> Maximum number of iterations:  1\n",
            "> Backend:  qasm_simulator\n",
            "(Shot number: 131072)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubD8Wl1yoiXQ",
        "outputId": "9f6a4808-fbb0-4081-8153-6d69ec9146c0"
      },
      "source": [
        "myAdapt.run()\n",
        "data = myAdapt.data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "*** Adapt Iteration 1 ***\n",
            "\n",
            "Calculating gradients and selecting the next operator...\n",
            "\n",
            "Non-Zero Gradients (calculated, tolerance E-5):\n",
            "Operator 8: 0.3608964040523989\n",
            "Operator 9: 0.3608964040523989\n",
            "Operator 10: 0.3608964040523989\n",
            "Operator 11: 0.3608964040523989\n",
            "Operator 12: 0.3608964040523989\n",
            "Operator 13: 0.3608964040523989\n",
            "Operator 14: 0.3608964040523989\n",
            "Operator 15: 0.3608964040523989\n",
            "Total gradient norm: 1.020769178445166\n",
            "Selected: 1j [X0 X1 Y2 X3], index 8\n",
            "(gradient: 0.3608964040523989)\n",
            "\n",
            "Optimizing energy with indices [8]...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/qiskit/utils/deprecation.py:62: DeprecationWarning: Using a qobj for run() is deprecated as of qiskit-aer 0.9.0 and will be removed no sooner than 3 months from that release date. Transpiled circuits should now be passed directly using `backend.run(circuits, **run_options).\n",
            "  return func(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of function evaluations: 16\n",
            "Optimized energy:  -1.135226863371902\n",
            "Optimized coefficients:  [0.0751]\n",
            "Energy Change:  -0.01791831476342831\n",
            "Performance ratio:  0.04964946882880754\n",
            "\n",
            "Current average performance ratio:  0.04964946882880754\n",
            "Current 10-last average performance ratio:  0.04964946882880754\n",
            "Current average performance ratio of the ansatz:  0.04964946882880754\n",
            "Current ansatz: [1j [X0 X1 Y2 X3]]\n",
            "\n",
            "Energy Changes Associated with the Indices: [-0.01791831476342831]\n",
            "Performances Associated with the Indices:  [0.04964946882880754]\n",
            "\n",
            "The maximum number of iterations (1) was hit before the convergence criterion was satisfied.\n",
            "(current gradient norm is 1.020769178445166 > 0.15)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Pi0etoLuI2J"
      },
      "source": [
        "## Plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akVKcq-uuMSx"
      },
      "source": [
        "### Single Run"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Q_F1ZYg0foje",
        "outputId": "3f0b3d47-f4b8-442a-9d0d-ced8d04e586c"
      },
      "source": [
        "data.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jdU12YP5NNh"
      },
      "source": [
        "### Single Iteration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "NdgTJOD0gpYg",
        "outputId": "53dbdfb7-9a89-4cc3-8a10-e47ee133fe26"
      },
      "source": [
        "# Upload evolution dict from computer\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "  \n",
        "myFile = open(fn, \"rb\")\n",
        "evolution = pickle.load(myFile)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-89a12db8-1791-4f41-b52c-ca1acf128c3f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-89a12db8-1791-4f41-b52c-ca1acf128c3f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving H2_ibmq_belem_iteration1 (1).pkl to H2_ibmq_belem_iteration1 (1) (3).pkl\n",
            "User uploaded file \"H2_ibmq_belem_iteration1 (1).pkl\" with length 1396 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_OT3Z7hY6h_W"
      },
      "source": [
        "# Use last run\n",
        "\n",
        "evolution = myAdapt.optEvolution"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Eo5pgrRhmOhE",
        "outputId": "5fff53f2-3cff-453c-c601-ff6dec71c9d8"
      },
      "source": [
        "nfev = evolution[\"nfev\"][-1]\n",
        "energies = []\n",
        "for i in range(nfev):\n",
        "  optimizedCoefficients = list(evolution[\"parameters\"][i])\n",
        "  energy = myAdapt.calculateEnergy(optimizedCoefficients,data.result[\"indices\"])\n",
        "  energies.append(energy)\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=[8,8])\n",
        "\n",
        "nfev = evolution[\"nfev\"][-1]\n",
        "\n",
        "title = \"Adapt-VQE Iteration 1: Evolution of the Optimization\\n{}, \".format(name)\n",
        "\n",
        "if backend.name() == \"statevector_simulator\":\n",
        "  backendName = \"Aer State Vector Simulator\"\n",
        "\n",
        "elif backend.name() == \"qasm_simulator\":\n",
        "  backendName = \"Aer QASM Simulator\"\n",
        "\n",
        "else:\n",
        "  backendName = backend.name()\n",
        "\n",
        "if backendName != \"Aer State Vector Simulator\":\n",
        "  title += \"{} shots, \".format(myAdapt.shots)\n",
        "title += \"{}\".format(backendName)\n",
        "\n",
        "ax1.set_title(title)\n",
        "\n",
        "color1 = 'b'\n",
        "ax1.plot(range(nfev), energies, '--o', color = color1)\n",
        "ax1.set_xlabel(\"Optimization Iteration\")\n",
        "ax1.set_ylabel(\"Energy (au)\")\n",
        "\n",
        "# UCCSD graph scale\n",
        "#ax1.set_ylim(-1.15,-0.05)\n",
        "\n",
        "minAccuracy = exactEnergy - chemicalAccuracy\n",
        "maxAccuracy = exactEnergy + chemicalAccuracy\n",
        "l = ax1.axhspan(minAccuracy, maxAccuracy, alpha=0.3, color = 'cornflowerblue')\n",
        "\n",
        "plt.axhline(y = exactEnergy, color = 'cornflowerblue' , linestyle = '-')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4YMazt8qjAb"
      },
      "source": [
        "### Bond Dissociation Graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rya5kzUbqiNN"
      },
      "source": [
        "%%capture\n",
        "\n",
        "backend = Aer.get_backend('qasm_simulator')\n",
        "#backend = Aer.get_backend('statevector_simulator')\n",
        "shots = 2**13\n",
        "zoom = True\n",
        "name = molecule.description\n",
        "\n",
        "threshold = 2.5*10**(-1)\n",
        "adaptEnergies = []\n",
        "exactEnergies = []\n",
        "hfEnergies = []\n",
        "\n",
        "\n",
        "if zoom:\n",
        "\n",
        "  if name == \"H2\":\n",
        "    start = 0.3\n",
        "    stop = 0.45\n",
        "    points = 20\n",
        "  \n",
        "  if name == \"H4\":\n",
        "    start = 0.7\n",
        "    stop = 1\n",
        "    points = 20\n",
        "\n",
        "else:\n",
        "\n",
        "  if name == \"H2\":\n",
        "    start = 0.1\n",
        "    stop = 1.1\n",
        "    points = 50\n",
        "\n",
        "  if name == \"H4\":\n",
        "    start = 0.25\n",
        "    stop = 2.0\n",
        "    points = 50\n",
        "\n",
        "rList = np.linspace(start,stop,points)\n",
        "\n",
        "for r in rList:\n",
        "\n",
        "  if name == \"H2\":\n",
        "    geometry = [['H',[0,0,0]],['H',[0,0,2*r]]]\n",
        "    basis = 'sto-3g'\n",
        "    multiplicity = 1\n",
        "    charge = 0\n",
        "    h2 = MolecularData(geometry,basis,multiplicity,charge,description='H2')\n",
        "    molecule = run_pyscf(h2,run_fci = True,run_ccsd = True)\n",
        "\n",
        "  if name == \"H4\":\n",
        "    geometry = [('H', (0,0,0)), ('H', (0,0,2*r)), ('H', (0,0,4*r)), \n",
        "                ('H', (0,0,6*r))]\n",
        "    basis = 'sto-3g'\n",
        "    multiplicity = 1\n",
        "    charge = 0\n",
        "    h4 = MolecularData(geometry,basis,multiplicity,charge,description='H4')\n",
        "    molecule = run_pyscf(h4,run_fci = True,run_ccsd = True)\n",
        "\n",
        "  qinstance = QuantumInstance(backend, \n",
        "                              shots = shots)\n",
        "\n",
        "  myAdapt = AdaptVQE(qubitPool,\n",
        "                    molecule,\n",
        "                    qinstance = qinstance,\n",
        "                    verbose = False,\n",
        "                    threshold = threshold,\n",
        "                    maxIterations = 1,\n",
        "                    shots = shots,\n",
        "                    referenceDeterminant = None\n",
        "                    )\n",
        "  \n",
        "  myAdapt.prepare()\n",
        "  myAdapt.run()\n",
        "  data = myAdapt.data\n",
        "\n",
        "  hfEnergies.append(data.initialEnergy)\n",
        "  exactEnergies.append(molecule.fci_energy)\n",
        "  adaptEnergies.append(data.result[\"energy\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "bWt2u6klqd9d",
        "outputId": "893afcef-2247-4af6-f42a-d1d4a9786711"
      },
      "source": [
        "if backend.name() == \"statevector_simulator\":\n",
        "  backendName = \"Aer State Vector Simulator\"\n",
        "\n",
        "elif backend.name() == \"qasm_simulator\":\n",
        "  backendName = \"Aer QASM Simulator\"\n",
        "\n",
        "else:\n",
        "  backendName = backend.name()\n",
        "\n",
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "minEnergies = [energy - chemicalAccuracy for energy in exactEnergies][start:stop]\n",
        "maxEnergies = [energy + chemicalAccuracy for energy in exactEnergies][start:stop]\n",
        "\n",
        "fig = plt.subplots(figsize = [8,8])\n",
        "\n",
        "plt.plot(rList[start:stop],hfEnergies[start:stop],color = 'g',label = 'Hartree Fock Ground Energy')\n",
        "plt.plot(rList[start:stop],adaptEnergies[start:stop],'o',color = 'b',label = 'ADAPT-VQE Ground Energy')\n",
        "plt.plot(rList[start:stop],exactEnergies[start:stop],color = 'r',label = 'Exact Diagonalization Energy')\n",
        "plt.fill_between(rList[start:stop],minEnergies,maxEnergies,color='cornflowerblue',alpha=0.3)\n",
        "\n",
        "plt.ylabel(\"Energy (au)\")\n",
        "plt.xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"ADAPT-VQE for {name}\\n\"\n",
        "if backendName != \"Aer State Vector Simulator\":\n",
        "  title += \"{} shots, \".format(shots)\n",
        "title += \"{}\".format(backendName,threshold)\n",
        "\n",
        "plt.title(title)\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f861e811b10>"
            ]
          },
          "metadata": {},
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ynxE6UzTFPMJ"
      },
      "source": [
        "### Error Along Bond Dissociation Graph"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-aYzcwZ0G2jQ"
      },
      "source": [
        "Calculate error along bond dissociation graph for H2, along with the number of shots per run per interatomic distance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_53kGXrpuQXz",
        "outputId": "dcb519c8-f90d-4fbc-f87f-f3719ae103ca"
      },
      "source": [
        "# Obtain number of Pauli strings in the Hamiltonian\n",
        "\n",
        "hamiltonian = molecule.get_molecular_hamiltonian()\n",
        "qubitHamiltonian = jordan_wigner(hamiltonian)\n",
        "\n",
        "qHamiltonianOps = list(qubitHamiltonian.get_operators())\n",
        "\n",
        "# Create list of Pauli strings in Hamiltonian\n",
        "hamiltonianStrings = []\n",
        "\n",
        "for term in qubitHamiltonian.get_operators():\n",
        "\n",
        "  pauliString = list(term.terms.keys())[0]\n",
        "  hamiltonianStrings.append(pauliString)\n",
        "\n",
        "\n",
        "# Get total number of Pauli strings in the Hamiltonian.\n",
        "# Exclude identity (does not imply measurements)\n",
        "hamiltonianStringCount = len(hamiltonianStrings)-1\n",
        "print(hamiltonianStringCount)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1OtByCdPvY2R",
        "outputId": "06e19bb9-5d10-40d0-89d2-d0d4b7a0f961"
      },
      "source": [
        "# Get total number of Pauli strings in the observables [H,A], that are measured\n",
        "#to evaluate the gradients.\n",
        "# If strings are repeated between the Hamiltonian and the operators [H,A], \n",
        "#one can recycle measurements. Each string should be included only once in the\n",
        "#total shot count calculations.\n",
        "\n",
        "poolMeasStrings = []\n",
        "totalStrings = 0\n",
        "\n",
        "for operator in qubitPool:\n",
        "\n",
        "  observable = qubitHamiltonian*operator - operator*qubitHamiltonian\n",
        "\n",
        "  for term in observable.get_operators():\n",
        "    pauliString = list(term.terms.keys())[0]\n",
        "    totalStrings += 1\n",
        "\n",
        "    if pauliString not in poolMeasStrings \\\n",
        "    and pauliString not in hamiltonianStrings:\n",
        "      poolMeasStrings.append(pauliString)\n",
        "\n",
        "gradientsStringCount = len(poolMeasStrings)\n",
        "\n",
        "print(\"Strings that need to be measured to evaluate the gradients\"\n",
        "      \" each iteration: \", gradientsStringCount)\n",
        "print(\"Total number of strings in the observables (including repeats): \"\n",
        "      ,totalStrings)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Strings that need to be measured to evaluate the gradients each iteration:  24\n",
            "Total number of strings in the observables (including repeats):  152\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eyls7GtYF39y"
      },
      "source": [
        "%%capture\n",
        "\n",
        "# Number of runs over which to take the median and IQR for each point\n",
        "runs = 10\n",
        "\n",
        "# Circuit repetitions\n",
        "shots = int(2**13/1.35)\n",
        "shots = int(2**13)\n",
        "\n",
        "medianEnergies = []\n",
        "exactEnergies = []\n",
        "totalShots = []\n",
        "nfevList = []\n",
        "numberMeasuredObs = []\n",
        "\n",
        "backend = Aer.get_backend('qasm_simulator')\n",
        "\n",
        "zoom = True\n",
        "\n",
        "if zoom:\n",
        "  start = 0.3\n",
        "  stop = 0.4\n",
        "  points = 10\n",
        "\n",
        "else:\n",
        "  start = 0.1\n",
        "  stop = 1.1\n",
        "  points = 50\n",
        "  points = 50\n",
        "\n",
        "rList = np.linspace(start,stop,points)\n",
        "\n",
        "for r in rList:\n",
        "  \n",
        "  geometry = [['H',[0,0,0]],['H',[0,0,2*r]]]\n",
        "  name = \"H2\"\n",
        "  basis = 'sto-3g'\n",
        "  multiplicity = 1\n",
        "  charge = 0\n",
        "  h2 = MolecularData(geometry,basis,multiplicity,charge,description='H2')\n",
        "  molecule = run_pyscf(h2,run_fci = True,run_ccsd = True)\n",
        "\n",
        "  finalEnergies = []\n",
        "  nfevs = []\n",
        "\n",
        "  for _ in range(runs):\n",
        "\n",
        "    qinstance = QuantumInstance(backend, \n",
        "                                shots = shots)\n",
        "\n",
        "    myAdapt = AdaptVQE(qubitPool,\n",
        "                      molecule,\n",
        "                      qinstance = qinstance,\n",
        "                      verbose = False,\n",
        "                      maxIterations = 1,\n",
        "                      shots = shots,\n",
        "                      referenceDeterminant = None\n",
        "                      )\n",
        "  \n",
        "    myAdapt.prepare()\n",
        "    myAdapt.run()\n",
        "    data = myAdapt.data\n",
        "\n",
        "    finalEnergies.append(data.result[\"energy\"])\n",
        "\n",
        "    nfev = myAdapt.optEvolution['nfev'][-1]\n",
        "    nfevs.append(nfev)\n",
        "\n",
        "  exactEnergies.append(molecule.fci_energy)\n",
        "\n",
        "  median = np.median(finalEnergies)\n",
        "  medianEnergies.append(median)\n",
        "\n",
        "  # Calculate average number of function evaluations for this interatomic distance\n",
        "  nfev = np.average(nfevs)\n",
        "\n",
        "  # Append average number of function evaluations for this interatomic distance\n",
        "  nfevList.append(nfev)\n",
        "\n",
        "  # Total number of measured observables\n",
        "  # = # energy evaluations (nfev) + # operators in pool\n",
        "  # only 1 iteration here\n",
        "  measuredObservables = nfev + len(qubitPool)\n",
        "\n",
        "  numberMeasuredObs.append(measuredObservables)\n",
        "\n",
        "  # # shots to evaluate the energy =\n",
        "  # (# energy evaluations) * (# strings in Hamiltonian) * (# shots per string)\n",
        "  # Only one iteration here, so only one nfev\n",
        "  energyShots = nfev * hamiltonianStringCount * shots\n",
        "\n",
        "  # # shots to evaluate the gradients = (# different strings) * (# shots)\n",
        "  # Again, only one iteration. Would come multiplied by #iter otherwise\n",
        "  gradientShots = gradientsStringCount * shots\n",
        "  \n",
        "  # Add total number of shots used in obtaining the energy for this interatomic\n",
        "  #distance\n",
        "  totalShots.append(energyShots + gradientShots)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "ke3107Fc53Tk",
        "outputId": "e5c6eeb8-ea76-4148-9e99-3e04fab3f89b"
      },
      "source": [
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "adaptErrors = [np.abs(medianEnergy - energy) for (medianEnergy, energy) in \\\n",
        "               zip(medianEnergies, exactEnergies)][start:stop]\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize = [8,8])\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        adaptErrors[start:stop], \n",
        "        '--o',\n",
        "        color = 'b')\n",
        "\n",
        "l = ax1.axhspan(0, chemicalAccuracy, alpha=0.3, color = 'cornflowerblue')\n",
        "\n",
        "ax1.set_ylabel(\"Error (au)\")\n",
        "ax1.set_xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"ADAPT-VQE: Error in the Energy\\n\"\n",
        "title += \"{} shots, {}\".format(shots,name)\n",
        "\n",
        "ax1.set_title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'ADAPT-VQE: Error in the Energy\\n8192 shots, H2')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "_yUzNA_SDwj2",
        "outputId": "480b1fdb-bf24-427d-c3d7-87e890a46e39"
      },
      "source": [
        "# Plot average total number of measured observables per run\n",
        "\n",
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize = [8,8])\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        numberMeasuredObs[start:stop], \n",
        "        '--o',\n",
        "        color = 'b')\n",
        "\n",
        "ax1.set_ylabel(\"Average Number of Measured Observables (total per run)\")\n",
        "ax1.set_xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"ADAPT-VQE: Measured Observables\\n\"\n",
        "title += \"{} shots, {}\".format(shots,name)\n",
        "\n",
        "ax1.set_title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'ADAPT-VQE: Measured Observables\\n8192 shots, H2')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "UUeE108fD_Vx",
        "outputId": "c4a4bff0-7d13-44ee-f8fe-cf72d19ac8ca"
      },
      "source": [
        "# Plot average total number of shots per run\n",
        "\n",
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize = [8,8])\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        totalShots[start:stop], \n",
        "        '--o',\n",
        "        color = 'b')\n",
        "\n",
        "ax1.set_ylabel(\"Average Number of Shots (total per run)\")\n",
        "ax1.set_xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"ADAPT-VQE: Number of Shots\\n\"\n",
        "title += \"{} shots, {}\".format(shots,name)\n",
        "\n",
        "ax1.set_title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'ADAPT-VQE: Number of Shots\\n8192 shots, H2')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C134TOAx4wcy+DfQAugKfAX8DfgZ0SC48EZF4Lr4YFixYdd+CBWH/BhvAmDGrHjODCy8Mzx95BCZMWPV4mzZwzjnh+ahR8P77qx5fe204/fTw/J57YPLkVY937gwnnxye33knfPTRqsc32giOOy5/3EOG5P+ZJf3M3Yt3crOewMPunqnn2NqEavltgQnAFsAphCT/PXc/3cwGAue7+4F5rnEqcCpA9+7dt582bVqBfwoRkfxatAgl4LrM4Iwz4OqrV93fsiUsWxaen3gi3HLLqsc7doQvvwzPjzwS7rtv1ePdusH06eH5AQfA44+venyrreCdd8Lz3XaDl15a9fi3vw2vvpo/7hUr6v9ZJV3MbKy7D6j3WFIJvs77DJgCbANcBBwDLAPaAmsDI939R41db8CAAT6m7q2yiEiR9ewZqrfr6tEDJk6E5cu/eaxt27BdujT/8SVL6k+2+Y6bhVoAgMWLv5nEs8fzxT116jf3S/rkS/CJDZMzs45m1jp6eTLwvLvPdfeL3L2ru/cEBgPPxEnuIiJJufLKlQk3q127sH+NNcKxuo+sxo63bt3049nkDuF5Q8evvDLEWV/cUv6KOUzubuBlYHMz+8jMTjKz08zstOgtWwJvm9n7wAGEXvMiImVnyJCVbdZmoQQ8dGj627GHDAlxZm8YyiVuiaeoVfSlpip6EUnSjBmw4YZJR9F0DzwAs2aF/gBSXvJV0SfZi15EpKKUY3IHOPjgpCOQYtBUtSIiq+k3v4HDD6+/s1w5WLEC3n33m8PppLwpwYuIrAZ3uO02mDMnDH8rR4sXQyYDN92UdCRSSErwIiKrYdw4mDQJBg9OOpLmW3NN2HRTeLvelUOkXCnBi4ishuHDoVUrOOywpCNZPZmMEnylUYIXEWkm95Dgv/td6NQp6WhWTyYTJuVZtCjpSKRQ1IteRKSZFi8OQ8u23z7pSFZf376hk+D770O/fklHI4WgBC8i0kxt28KllyYdRWF85zvw0EPQq1fSkUihqIpeRKQZli8PCbFSqrQ32AAOPDCsVCeVQQleRKQZXngBfvCDMAtcpXj55cr6eaqdEryISDPU1ISFWQ5scDHr8vP3v8O55yYdhRSKEryISBMtXQojRsBBB0H79klHUzh9+8LkyTB/ftKRSCEowYuINNEzz8AXX5T35Db1yWTC9t13k41DCkMJXkSkif7979AZbf/9k46ksLIJXhPeVAYleBGRJvrzn2H8+JXrqFeK3r3DzzRhQtKRSCFoHLyISBOZVeZ48ZYt4Y03oGfPpCORQlAJXkSkCc48Ey66KOkoimeLLSqvZqJaKcGLiMS0YAHcfDPMmpV0JMUzYQKcfz589VXSkcjqUoIXEYnp0UfDELJK6z2f68MPQx8DdbQrf0rwIiIx1dSEKV333DPpSIpHPekrhxK8iEgM8+bBI4/AkUeGzmiVqls36NBBCb4SKMGLiMSwYAEcfzwcc0zSkRSXWSjFv/NO0pHI6tIwORGRGDbYAK67LukoSiOTgRdfTDoKWV0qwYuINGLOHHjlFXBPOpLSuOYaTVdbCZTgRUQaMXIk7LwzjBuXdCSl0bp10hFIISjBi4g0oqYmTOO63XZJR1IaixbB0UfDXXclHYmsDiV4EZE8Zs6Ep58OY9/Nko6mNNq0gSefhNGjk45EVocSvIhIHiNGwPLlMGhQ0pGUTrYnvYbKlTcleBGRPB58ELbcErbeOulISis7VK5aOhZWIg2TExHJY+RImD69eqrnszIZmDsXPvooTH4j5UcleBGRPNq2hW99K+koSq9fP+jfH2bPTjoSaS4leBGRBhxzDNx4Y9JRJGPnncPa8P36JR2JNJcSvIhIPaZOhTvvhC++SDoSkeZRghcRqcc994RtNfWer+v882GffZKOQppLCV5EpB7Dh8OOO0KvXklHkhx3eOmlMExQyo8SvIhIHbW1YVraai69A/TtG2a1mzIl6UikOZTgRUTqWLIEDjkkrP1ezTKZsNWEN+VJCV5EpI5MBkaNgq5dk44kWVttFbZK8OVJCV5EJMenn4Ye9AJrrQUnnACbbpp0JNIcSvAiIjmuvRY220wTvGTdfHNYWU7KjxK8iEjEPfSe/853oFOnpKNJj/nz1ZO+HCnBi4hExo2DSZPC0rASjBoFHTrAf/+bdCTSVErwIiKR4cOhVSs49NCkI0mP3r1DzYY62pUfJXgREUISGzEC9ttP1fO5Nt8cWrZUgi9HWi5WRISwHOwrr8CXXyYdSbq0bQt9+ijBlyMleBGRSJcu4SGrymRg/Piko5CmUhW9iFS95cvh8MPhiSeSjiSdjjsOzj476SikqVSCF5Gq98ILMHKkes835MADk45AmkMleBGpejU10L49fP/7SUeSTu5h+OCHHyYdiTSFEryIVLWlS+G+++AHP4B27ZKOJp2WLw8ry11zTdKRSFMowYtIVXvmGZg1S0vD5tOqFWy5pXrSlxsleBGpai1awN57w/77Jx1JuvXtqwRfbpTgRaSq7bsvPPUUtGmTdCTplsnA9Okwd27SkUhcSvAiUrU+/BDmzEk6ivKQyYTtO+8kG4fEpwQvIlXrwgtD1fOKFUlHkn477xym8t1886Qjkbg0Dl5EqtKCBfDggzBkSGiHl/w6d4bDDks6CmkK/VqLSFV65JGwzrl6z8c3bhzcf3/SUUhcSvAiUpWGD4cNNoA990w6kvJxzTVw2mlJRyFxKcGLSNWZPz+U4I88MiyFKvFkMvDZZzBzZtKRSBxqgxeRqtO+PbzxhobGNVVuT/qBAxMNRWJQCV5EqtIWW0CvXklHUV40VK68KMGLSFWZPRt++EOYMCHpSMrPRhvBuutqRrtyoQQvIlVl1Ci4+25YtCjpSMqPGbz4IvzhD0lHInGoDV5Eqsrw4dC7NwwYkHQk5WmrrZKOQOJSCV5Eqsbnn8PTT8PgwaE0Kk1XWwu/+EX4t5R0U4IXkaoxYkSYllaT2zTfJ5/AVVeFUQiSbkrwIlI11lgjLAu79dZJR1K++vYNW/WkTz8leBGpGiefDI89pur51dGlS5gBUD3p069oCd7Mbjazz82s3l8DM1vXzEaZ2Vtm9pqZZaL93czsWTN718zeMbOzihWjiFSPDz+EpUuTjqIyZDJK8OWgmCX4W4H98xz/BTDe3bcBjgX+Hu1fBpzn7lsBOwH/Z2bqtykiq+XII2G//ZKOojJkMqEt3j3pSCSfoiV4d38emJ3nLVsBz0Tv/S/Q08w2cPdP3X1ctH8e8B6wSbHiFJHKN2UKvPqqEnyh/O53oUZETR3plmQb/JvAYQBm9m2gB9A19w1m1hPYFni1oZOY2almNsbMxszUCggiUo977gnbo45KNo5K0batkns5SDLB/w7oaGbjgTOAN4Dl2YNmthYwAjjb3ec2dBJ3H+ruA9x9QJcuXYods4iUoZoa2HFHzT1fKMuXw3HHwa23Jh2J5JPYTHZR0j4BwMwMmAJMjl6vQUjuw9x9ZFIxikj5q62F8ePhL39JOpLK0bIljB4Ny5bB8ccnHY00JLEEb2YdgQXuvgQ4GXje3edGyf4m4D1315+kiKyWTTeFZ5/VFKuFlsloLHzaFS3Bm9ndwECgs5l9BFwKrAHg7v8CtgRuMzMH3gFOij66K3AMMCGqvgf4hbs/WqxYRaRytWyptcuLoW9feOqpUIpvpVVNUqlo/y3ufnQjx18GvlXP/hcBdd8QkdX27rtwww3ws5+FpU6lcDIZWLIEJk2CLbZIOhqpj2ayE5GKddddcPXVoRQvhbXNNqHZ46uvko5EGqKKFRGpSO5hadi99oL11086msrTv7/a4NNOJXgRqUjjxoXq48GDk45EJBlK8CJNMGwY9OwJLVqE7bBhSUckDampCavHHXpo0pFUrssugz33TDoKaYiq6EViGjYMTj0VFiwIr6dNC68BhgxJLi6pX6tWYf75Tp2SjqRyrVgBL70EixaF2e0kXVSCF4np4otXJvesBQvCfkmfq65SDUux9e0bZrV7//2kI5H6KMGLxDR9etP2S3JmzNBKZ6WQyYStlo5NJyV4kZi6d2/afknG8uWw7bZw5plJR1L5+vQJ/RyU4NNJCV4kpiuvhHbtVt3Xrl3YL+nx/POhBL/HHklHUvlatw6Lzmy2WdKRSH3UyU4kpiFDYNYsOOuslfv++Ed1sEub4cOhfXv4/veTjqQ63HBD0hFIQ1SCF2mCvn3D9h//CNsW+gtKlaVL4b774Ac/+GZtixTP4sVhTnpJF309iTRBbW3YHnIIvPginHJKsvHIqp5+OtSyDBqUdCTV48knQ43JG28kHYnUpQQv0gQTJ4aS4cYbw667ao7ztNl9d7j7bth//6QjqR69eoWOjepolz5K8CJNUFsbeg63aBG+1M47D265JemoJKt9+zA1bZs2SUdSPXr1CpPcKMGnjxK8SBP8/vdw7bXhecuWocf2ddclG5MEzz0X/n/mz086kurSsmVYVU4JPn2U4EWaoG/fUDWfNWgQvP46fPBBcjFJcP31YVRD69ZJR1J9Mhkl+DRSgheJ6fPPQ3X8jBkr9x11VNgOH55MTBIsWAAPPghHHBEmXpHSGjwYzj03zE0v6RErwZvZumbW18x6m5luCqQqjRkDJ54Ikyev3Ne9O+yyixJ80h55JFTNq/d8Mg44IPRH0bDRdGlwohszWwf4P+BooDUwE2gLbGBmrwD/dPdnSxKlSApkh8j16bPq/hNPDO2/WlErOTU1sOGGmr0uKe7w4YdgBt26JR2NZOW737oP+BDY3d03d/fd3H2Au3cDfgccbGYnlSRKkRSYOBE6doTOnVfdf9JJcPvtSu5JcQ9Vwz/8oYYtJmnrrUMnR0mPBkvw7r5vnmNjgbFFiUgkpbJD5MzqP/7ee7DFFg0fl+Iwg1GjtHpcksxCB9R33kk6EskVtw1+EzPbxcz2yD6KHZhI2kycCN/6Vv3H7r47DBV6883SxiTw1VdhqxurZGUyMGGCbrTSpNHFZszs98Ag4F1gebTbgeeLGJdI6rz2GixZUv+xffcN1cPDh0P//qWNq5rNnh1mFfzLX+D005OOprplMmHhmc8/hw02SDoagXiryR0CbO7ui4sdjEiarb9+w8c6dw5JvqYGfvtblSZLZdSosNDJjjsmHYlkMmH79ttK8GkRp4p+MqCRpVLVxoyBK66AL79s+D2DB8PUqaGkL6VRUwObbgrbbZd0JLLddqGpauutk45EsuIk+AXAeDO73syuzj6KHZhImjzzDFx6af5xvoccEmZRu+ee0sVVzT7/PPy/DB6sGpM06Ngx/F/kq+mS0opTRf9g9BCpWrW14YtrnXUafs8668C//w3bb1+6uKrZffeF4XGDBycdiWS9807ojHrIIUlHIhAjwbv7baUIRCTN8vWgzzVwYNFDkcihh4a5B7Jtv5K8f/0LbrsN5sxRrUoaxOlFP4XQa34V7t67KBGJpFBtbZiOM45rrgkdv847r7gxVbuNNgqzCEp69O0L8+aFWe26d086GolTRT8g53lb4EigU3HCEUmfBQvCWOs4JXiAF16AZ5+Fs86CVnH+wqTJHngg/J8ce6xKimmS25NeCT55jXayc/dZOY+P3f1vwPdLEJtIKrRrFxYyOfvseO8fNAhmzoTRo4saVlW76ir4+9+V3NOmb9+w1dKx6RCnij53AEoLQole5RKpKi1axJ9r/oADoEOHMIRrn32KG1c1mjIFXn1V856n0brrwiabaMratIiTqP+c83wZMAU4qjjhiKTPHXeEavd//Svecphrrhl6EY8cCf/8Zxg6J4WTHYZ4lL6FUumxx6Br16SjEGgkwUdrv//L3bXatVStJ58M1e1NWev6hz+EGTPCWG192RVWTQ3stBP07Jl0JFIfTXSTHnm/stx9BXBBiWIRSaXsKnJNsf/+8MQTSu6FNnduWMxEY9/Ta+pUuOwy+OijpCOROFX0T5nZ+cBwYH52p7vPLlpUIikycWLzq4NnzAjtkm3aFDamarX22jB+fJjgRtJp5ky4/HLo1083uEmLU+k4CPg/wupx2XXgxxQzKJG0mDUrrFjW1BI8hPnrN9kEHnmk8HFVI/cwZBGa1lwipbXVVmGrjnbJizNMrlc9D01yI1Vh5nJEjJwAACAASURBVMywmMkWWzT9s/37h1XmamoKH1c1evtt6NIlNH1IerVvD716aahcGmi4m0geW2wBkyY177OtWsERR8Att8DXX8NaaxU2tmpTUwOLFoUbJ0m3TEYJPg1U0SVSRIMHw8KF8NBDSUdS3txDgt97b61WVg4ymdDJbvnypCOpbnkTvAXdShWMSNqcfTacckrzP7/rrqEdXtX0q2fsWJg8OcwSKOn3y1+GvistWyYdSXXLW0Xv7m5mjwIa2ShV6bnnYOONm//5Fi3gzjtDO740X00NrLFGWEFO0q9du6QjEIhXRT/OzHYoeiQiKeMehsg1pwd9roEDoZvqwVbLccfB9ddDJy1zVRbc4fTT4cYbk46kusVJ8DsCL5vZB2b2lplNMLO3ih2YSNI+/TQsMhN3Fbl8HnwwTP4hzbP11nDCCUlHIXGZhdkfNUQ0WXF60e9X9ChEUqi2NmxXtwQP8J//wJ/+BD/9aRg6J/HddRdssEHoYCflo2/fMCmRJCfOOPhpQDdgr+j5gjifEyl3LVvCnns2bwx8XYMHhx7FI0eu/rmqyfLlcO65YdEeKS+ZDHzwwcrJiaT0Gk3UZnYpcCFwUbRrDeDOYgYlkga77x6qGQvRft6vX6jqV2/6pnnuOfjsM809X44ymdAW/957SUdSveKUxA8FfkA0D727fwJ0KGZQImngXrhzmYUkNXp0aNuX/IYNC6vF7b13+Lf7+uukI5KmymRgs81gzpykI0mH7O90ixZhO2xY8a8ZJ8EvcXcHHMDM2hc3JJF02H57OPPMwp1v0KDQLqlVtvIbNgxOPRWmTQuv3UPfhVJ8IUrhbL55GIWy115JR5K83N9p97A99dTi/07HSfD3mNn1QEczOwV4CrihuGGJJGv58rBYxpprFu6cW20FEybADhp0mtfFF3+z3XbBgrBfpBwl9Tsdp5Pdn4D7gBHAt4Bfufs/ihuWSLKmT4clSwrTg76uhQth3rzCn7dSTJ/etP2SXn/6U5jNsdol9Tsdtzf8BOAFwpKxE4oXjkg6TJwYtoUYA5/riy/CXOrXX1/Y81aS7t2btl/Sa9myMER07tykI0lWUr/TcXrRnwy8BhwGHAG8YmYnFjcskWQVcgx8rs6dw7A79aZv2JVXfnO993btwn4pL337hm21rw1/5ZXQps2q+0rxOx2nBH8BsK27H+/uxwHbE4bNiVSsPn3CzGkbblj4cw8aFBZPae4ytJVu3XVhxYowLa0Z9OgBQ4fCkCFJRyZNlcmEbbUvHTtkCNx0U/hdLuXvdJyZ7GYBuS2G86J9IhVrv/3CoxiOOgouuACGD1fHsbrc4ZJLoFcveP/9sMCMlK8ePaB9eyX4Rx6B3XaDqVNLe904JfhJwKtmdlk06c0rQK2ZnWtm5xY3PJFkzJpV2HHwubp3Dx2Phg8vzvnL2f33w7hx8KtfKblXghYt4Jhjwnj4arVoERx9NFx+eemvHacE/0H0yHog2mqyG6lIS5aEjnC/+hVcemlxrvH730PbtuEmwqw41yhHI0aEjo0/+lHSkUihXHdd0hEk6/HHw6iZJGZjbDTBu3sC9x0iyZkyJbQB9+5dvGto6FD97rgDPvkEWsUpekjZWLYsbKvx/7WmJnSuTWLCHy0aI1JHsXrQ1zVmDPz858VrCigny5bB7NmhNmOTTZKORgrpP/8J7fAvvph0JKU3fz489BAccUQyNzdK8CJ1FGsMfF3jxoWqei2pGabszHask8rSs2do9qrGjnavvQaLFye3WJISvEgdtbVhiFanTsW9zmGHhbv6au9st3QpXHEFbLpp8W+qpPQ22igMfazGsfDf+U5YXGq33ZK5foOVBmb2D6IFZurj7gVchkMkPQ49FLbZpvjX6dwZ9tknJPirrqrezna33gqTJ8PDD1fvv0ElMwvj4auxBA/QpUty185Xgh8DjM3zEKlI++0Hp59emmsNHhzGxr72WmmulzaLF8Ovfw077QTf+17S0UixZBN8NfU3GTYslOC/+CK5GBoswbv7baUMRCQNsm2FW2wRppIstkMOCePiP/64+NdKoyeegA8/hJtvVum9kh16KHTrFppjWrdOOprSuOuuMCJnvfWSi6HRfn1m1oUwNe1WQNvsfnfXKr9Scf7737AOfE1NmFK22NZZJ5TgqzW5HXRQuKHaaqukI5Fi2nff8KgWs2aFm9dzz032bztOJ7thwHtAL+ByYCrwehFjEklMdohcKTt7mYVx99W2hOz8+WHbt2/13uBUk88+C7U11WDkyDD0M6ne81lxEvx67n4TsNTdn3P3EwGV3qUiZYfIFXsMfK4VK0IJ9uc/L901k/b11+Hf+O9/TzoSKZX+/cPskNVg+PDw+92/f7JxxBl6vzTafmpm3wc+AYo8gEgkGbW1sPHGsNZapbtmixbQrx/ce29IeNUw29fVV4fhQzvtlHQkUip9+1bPULmDDw59eJKumYpTgv+Nma0DnAecD9wInN3Yh8zsZjP73MzqHRxhZuua2Sgze8vMXjOzTM6x/c3sfTObZGZVVK6RpNXWlrb0njV4MMycCaNHl/7apTZnDvzpT3DggbDjjklHI6WSyYQEv2JF0pEU3xlnwEknJR1FvAT/pbvPcfe33f077r49MDvG524F9s9z/BfAeHffBjgW+DuAmbUErgUOIHTsO9rM1AVHSuJ3v4PLLiv9dQ84ADp0CJ37Kt1f/wpffhkmt5HqkcnAggWlXzK11B59NNzEpkGcBP+PmPtW4e7Pk/9GYCvgmei9/wV6mtkGwLeBSe4+2d2XADXAwTHiFFltu+8OAweW/rpt24YhcyNGhKF6lWrxYrj22jCL37bbJh2NlFImqqOt5AlvPvwQvv99uOaapCMJ8s1ktzOwC9ClzrrvawMtC3DtN4HDgBfM7NtAD6ArsAmQ29fyI6DBijwzOxU4FaB79+4FCEuq1SefhAln9toL1l679Nc/55xQVd+igieQbtMGxo6tjmpaWdXWW8Ntt8GAAUlHUjz33hu2pRhiG0e+r5LWwFqEm4AOOY+5wBEFuPbvgI5mNh44A3gDWN7Uk7j7UHcf4O4DuiQ5J6CUvWefDRNyfPRRMtffdtswm1uldrLLLhnavXtYgESqS/v2cOyxoRNrpaqpCfNobLZZ0pEE+Wayew54zsxudfdpZrZWtP/rQlzY3ecCJwCYmQFTgMnAmkC3nLd2Bap0ni8ppdra0Ot1002Ti2Hq1DCr20UXwZprJhdHMfzsZ/Duu/DII9CyEHWAUnYmTgy/AwdXYKPr5Mnw+uvwxz8mHclKcSoDO5jZG8A7wDtmNja3x3tzmVlHM8tOWngy8HyU9F8H+phZr+j4YODB1b2eSGMmToQePUI1clImTQpzsz/2WHIxFMPHH8N114WVxZTcq9eNN8JRR4UpayvNk0+G7VFHJRtHrjgJfihwrrv3cPcehOFyQxv7kJndDbwMbG5mH5nZSWZ2mpmdFr1lS+BtM3uf0GP+LAB3Xwb8FPg3YQa9e9y9SkZPSpJqa5NfrnTgQFh//crrTf/b34Yq+mqZ6ETql8mETqSTJiUdSeH9+MehFJ+mrmBxWvvau/uz2RfuPtrM2jf2IXc/upHjLwP1fp26+6PAozFiEykI91CCT3rilVat4Igj4JZbwmxvpZxwp1imTYMbbgjjgnv1SjoaSVK2J/0778CWWyYbSzGk7fc7Tgl+spldYmY9o8cvCW3lIhXltdfgvPOSjiL0pF+4EB56KOlICuPPfw59Gy6+OOlIJGlbbBFGiVTaULk//AF+9CNY3uRu4sUVpwR/ImGRmZGAAy8QdY4TqRRmsPnmSUcR7LprmE2vUhbmuPLKMDa4W7fG3yuVbc01QyfWSkrw7qFjbBr7l8RJ8Pu4+5m5O8zsSODe4oQkUnqvvAJjxsDJJ4dJZ5LUogW89176viyawz3M0LfffklHImlx332w4YZJR1E4b70F778f5rFImzhV9BfF3CdSth54IPyBpmUMeja5L1qUbByr4733YJttYPz4pCORNNlmm9CRtFLU1IS/18MPTzqSb8o3k90BwPeATczs6pxDawPLih2YSCnV1oaqw7QkeAid7ebPL98hc5ddFsb1d+2adCSSJp98Ema0Gzw4fZ3Smso9LA27zz7QuXPS0XxTvhL8J8BYYFG0zT4eBFThJhVl4sRkVpHLp0+fMLb2iy+SjqTp3nwT7rkHzj47nV98kpyvvoJf/AL+85+kI1l9ixbBkUeGIXJplG8muzeBN81smLtX4LQEIsGKFSHB77tv0pGsavDgsLrdiBHp/QJpyKWXwjrrwLnnNv5eqS59+sAaa1RGR7s114Tf/z7pKBrWYAnezB4ys4MaONbbzK4wsxOLF5pIaXz6abgTT1sJfpttQs/+4cOTjqRp3ngj9Gk47zxYd92ko5G0WWON8Htd7gl+xQp4+umVayykUb4q+lOA3YH/mtnrZvaomT1jZpOB64Gx7n5zSaIUKaJNNoG5c8M41jQxC6X40aPDTUi52HpruP12OOuspCORtMpkyj/Bv/RSaHu/N8XjyfJV0c8Afgb8zMx6AhsBC4Fad19QkuhESqRDh6QjqN+xx4bx42mNrz6tWsExxyQdhaRZJgOjRsGCBdCuXdLRNM/w4aGK/qB667nTwdw96RgKZsCAAT5mzJikw5Ayc9ttMGVK6PUtq2fwYNh7bzjllKQjkTSbPz8s6pSmUStNsWxZqPnbY4/kS/BmNtbdB9R3LM44eJGKdt99MHJk0lE07Kuv4JprklunPq5nnw2lmvnzk45E0q59+/JN7hCazT7/PNzQppkSvFS9NKwil8/s2XDGGeleYc4dLrkklGpOO63x94tccEFYQrgcPfxwWAjqe99LOpL8mpTgzWxdM9umWMGIlNqyZWGJx7T1oM/VuzfssEO6E/y//x06HV18cfJT/Up5ePZZuP/+pKNonj//OSxOteaaSUeSX6MJ3sxGm9naZtYJGAfcYGZ/KX5oIsU3dWpI8mkuwUOoChw7Np3raGdL7z16hCVhReIo5570LVuWx3K3cUrw67j7XOAw4HZ33xHYp7hhiZTGZ59Bx47pLsFDmC0L0jkm3gz++Ef45z+hdeuko5Fy0bdvmLZ29uykI2maCy6Ayy9POop44iT4Vma2EXAU8HCR4xEpqV13DV8wu+6adCT5desGu+8emhPSaODA9LdHSrpkMmH7zjvJxtEUixbB9dfD9OlJRxJPnH6MlwP/Bl5099fNrDcwsbhhiZSOWdIRxPPkk2FoUZqMGhV6FP/2t6FntEhcmQx07w5z5iQdSXyPPQbz5sGgQUlHEk+cBP+pu/+vY527T1YbvFSKM86ATp3Ko8otm9yXLUvHEKPly0OnOjP4i74RpIm6dYNp05KOomlqasLiSXvtlXQk8cSpov9HzH0iZeehh9LZca0hv/oV9O8fOrYl7e67w5rvl1++cv16kUo1f34YHnfkkem4wY4j33rwOwO7AF3MLHdNqLUB/TlL2Vu0KLSlnXBC0pHE161baLMcPx623Ta5OJYuDYm9Xz847LDk4pDy9s9/wq23wquvpr+pbM4cOOKI9K1ZkU++EnxrYC3CTUCHnMdc4IjihyZSXB98EErCae9Bn+uww0LpIekx8bffHmo+rrgCWmi6LGmmZcvg9dfDaJa023jjMK31LrskHUl8+RabeQ54zsxudfdpZrZWtP/rkkUnUkS1tWGb9jHwudZbL6xbP3x4WCs+qVLPHnuE9vc0L7Qh6Zfbk37DDZONJZ9588J6FVtvnf6ahlxx7r07mNkbwDvAO2Y21swyRY5LpOjMwh9sOZXgIfTgnTYtVGsmpU8f+M1vyuvLTtInm+DTPuHNqFGhOWrcuKQjaZo4CX4ocK6793D3HsB50T6RsnbIIfDWW7DOOklH0jSHHBKSa/fupb/2woVw4omhc53I6lp/fejSJf0JvqYmzNS43XZJR9I0cRJ8e3d/NvvC3UcDGvEqkpB11gnV4xtvXPprX3893HJLebSZSnkYNAg23TTpKBo2a1aYg2LQoPKrsYrT2X+ymV0C3BG9/hGQ0vm0ROLbdtvwR/vznycdSdMtWQIPPgibbx6aGUph/ny46qqw3vvAgaW5plS+f6R80PXIkaEzYNqXhq1PnBL8iUAXYGT06BLtEylb8+aFoWZpGE/eHEuXwnHHlXa5zWuuCWtg//rXpbumVIcVK0ISTaNRo0Kfk/79k46k6RpN8O7+pbuf6e7bRY+z3P3LUgQnUiwTo8mWy6kHfa727eHAA+Hee0vzxTh3LvzhD2G++Z13Lv71pHqMHw8dOoQlh9PonntgxIjyq56HeMvFfsvMhprZE2b2TPZRiuBEiiWb4MutB32uwYPhiy/gmRL9NZ56ahj3LlJIPXvCggXp7Wi31lqlawYrtDht8PcC/wJuBJYXNxyR0siOgd9ss2TjWB0HHBBKPsOHw3e/W9xrrb12aH8XKbSOHWGTTdK5qtyPfwy77QbHHJN0JM0TJ8Evc/cStvSJFF/v3mHKyXbtko6k+dq2DUPmJkwIfQmKVYV4ww3QtWu4oRAphkwmfSX4Dz+EoUOTGY5aKA1W0ZtZJzPrBDxkZqeb2UbZfdF+kbI1ZAjccUfj70u7664r7jzeM2fCOeeEKTpFiiWTCXMrLE9RHfG994ZtuSwNW598JfixgAPZr44Lco450LtYQYkU25Il0Lp10lGsvuwa7CtWFGdO+D/8IUxuc9llhT+3SNaBB4a27sWL01OrVlMD229f3s14DX4luHsvd+8dbes+lNylbM2aBWuuGarfKsHtt0OvXqGjUiF9+mkYGvejH8EWWxT23CK5Bg4MN5FpSe4ffBAWwSnHse+58lXR72BmG+a8PtbMHjCzq1VFL+Vs4sRQ4t1oo6QjKYyNNw7L3j76aGHPe9VVYbz9r35V2POK1Gf27NDunQZffx06rh51VNKRrJ58lXrXA0sAzGwP4HfA7cAcNBe9lLFyXEUun4EDw5zew4cX9rzbbBNm+UvzNKJSOXbYAc4/P+kogn79wrj8cu5gB/nb4Fu6++zo+SBgqLuPAEaY2fjihyZSHBMnQsuWoVq7ErRqBUceCTfdFGbo69ChMOc9+eTCnEckjrT0pJ85M3T2S/PytXHlK8G3NLPsDcDeQO50GnGG14mkUm1tmFyjEjrZZQ0aBIsWwUMPrf65Jk8OvfOXLFn9c4nElcmEv82kf++uvRa6dQtNBuUuX4K/G3jOzB4AFgIvAJjZZoRqepGydNBBcOaZSUdRWLvuGqrTCzHj1hVXwLnnhlnyREolkwnTLmeb0JLgHnrP7747dKqAnmYNlsTd/UozexrYCHjC/X/LcrQAzihFcCLF8KMfJR1B4bVoUZiZ5t5/P8wPcPbZySxHK9Wrb9+wffvtkOyT8NZb4W/gnHOSuX6h5a1qd/dX6tmX4P2VyOpZuBBmzAidZ1q2TDqawnKHl14KbfI77dS8c1x2WRhCeOGFBQ1NpFGbbx6Grjb3d7cQamrC98LhhycXQyEVYWoMkfR67bUwTe3TTycdSXEce2zzJ6WZMCH0xD/jjNArX6SU2rSBU04J/WOS4B5mr9t3X+jcOZkYCi3fOPg2pQxEpBTKfZnYfMxCZ7unnmpe+/nChbDHHukZqiTVZ+pUePDBZK5tBqNHw5/+lMz1iyFfCf5lADOrgBm7RYLa2lBS6NYt6UiKY/DgMMRnxIimf/bb3w5fcOutV/CwRGK5/fawgFKhZ2WMq2vXlX0BKkG+BN/azH4I7GJmh9V9lCpAkUKqrQ0Tt1Ra+3vWNtuEtsyamqZ97tZbK2NYkJS3TCZUlb/3Xmmvu2JFWBL22WdLe91iy5fgTwN2BzoCB9V5HFj80EQKr7a2Mqvns8xCKf7tt8N0m3G88gqccALceGNxYxNpTG5P+lJ66SW4887QAbeS5Bsm9yLwopmNcfebShiTSNH85jewzjpJR1Fc550Hv/hF/Il8LrkEunSB008vblwijdl009CEVuoEX1MTRo8cdFBpr1tscWaku8PMzgT2iF4/B/zL3ZcWLyyR4jisChqXslPVuje+Tvzzz4dOeX/5S1iuUyRJrVrBlluWNsEvWwb33bdyydpKEmeY3D+B7aPtP4HtgOuKGZRIMXz0EbzwQlhzutI9/3xoi58+veH3uMMvfxkmtDnttNLFJpLPrbfCDTeU7nqjR8Pnn5f/0rD1iZPgd3D349z9mehxArBDsQMTKbT77w/DwKqhM1nXrmFI4D33NPyeBQtg3XXh4otD9aRIGvTrF35/S2XJEthlFzjggNJds1TiJPjlZva/BSPNrDewvHghiRTHxImhCq4SVolqTO/eYfnNfL3p27eHBx6An/ykdHGJNGbmTPjzn0s3J/33vhc62VXiTW6cBH8B8KyZjTaz5wiryp1X3LBECq+2Fvr0abxdulIMHgxjx8KkSd88NmbMykl/quXfQ8rD/PlhsqXRo4t/rU8/TW7MfSk0muDd/WmgD3AmYZGZzd29wkYLSjWYOLGyh8jVdeSRYTt8+Kr7V6wIU4IeeGBohxdJk+7dQ03bO+8U/1oXXghbbBH+JipRrLno3X2xu78VPaqgi5JUmiVLYMqUUIKvFt26hU50O+646v5Ro2D8+ND2rtK7pE2LFrDVVsXvSb9wYeiX893vhmtWojjD5ETKXosWoQd9tS2i8utfr/p6+XK49NJQahkyJJmYRBqTycDDDxf3Go89BvPmVWbv+ay89y0WVOis3VJNWrUKPWU32yzpSEpv0iR47rnw/J57QtXnZZdV7nS9Uv4ymbBgUjFHvAwfHiZ4GjiweNdIWt4E7+4OPFqiWESK5uWX4e67K7etLZ/TTgtt7u7w4Yehd322fV4kjU45JXS269SpOOefPx8eeij8HbSq4HrsOD/aODPbwd1fL3o0IkVy221hreejj046ktLr2ROefjqU2Lt3D9X2ldrmKJWh2DPKtWsHL74Ia69d3OskLU6C3xEYYmbTgPmAEQr32xQ1MpECqrYe9FnDhsFdd4Xn7jBtWijRt2ihNnhJt8suCyX4M88s/LnNYLvtCn/etIlzH78fsCmwFytXkquwKfml0mXHwFebiy8OvYVzLVgQ9ouk2TPP5J+JsbnmzIGTTy79krRJiDMOfhrQDdgrer4gzudE0mLBgjAPfTWW4Buaiz7fHPUiaZDJhKFyhZ6r4f774aabYO7cwp43jRpN1GZ2KXAhcFG0aw3gzmIGJVJI2ZncqrEE37170/aLpEUmE0rbn3xS2PPW1IR+Kd/+dmHPm0ZxSuKHAj8gtL/j7p8AHYoZlEghZTKh7bkSF5NozJVXhg5Fudq1C/tF0iyTCdtCTnjzxRdheeRBg6pjkqc4CX5JNFzOAcysfXFDEimsFi1CibXSe8zWZ8gQGDoUevQIX2g9eoTX6mAnaZfJhImpvvqqcOccOTKs/z5oUOHOmWZxetHfY2bXAx3N7BTgRKCEq/WKrJ5bb4VFi6p3zfMhQ5TQpfx06gSffVbYcy5bFpaM7t+/sOdNK/MYPRjMbF/gu9HLJ9z9yaJG1UwDBgzwMWPGJB2GpMxuu4Ux4NnZ3EREKoWZjXX3AfUdi9sbfgLwAvB89DzuhW82s8/NrN5WFDNbx8weMrM3zewdMzsh59gfon3vmdnVZtXQYiLFUK1j4EXK3W23wfbbF2YGys8+q76ZLOP0oj8ZeA04DDgCeMXMTox5/luB/fMc/z/gXXfvBwwE/mxmrc1sF2BXYBsgA+wA7BnzmiL/M2cOfP55dfagFyl3y5bBuHFhJcjVdfjh1dfRNk4J/gJgW3c/3t2PA7YnDJtrlLs/D+RbLsCBDlHpfK3ovcui/W2B1kAbwtC8ArfGSDWYODFsVYIXKT99+4bt6q4NP306vPRSaH+vJnES/CxgXs7redG+QrgG2BL4hFD1f5a7r3D3l4FngU+jx7/dvQrmHZJC+/jj0P6uErxI+dlqq7Bd3aFy2RnxqqX3fFaDvejN7Nzo6STgVTN7gFCyPhh4q0DX3w8YT5gGd1PgSTN7AVifkPi7Ru970sx2d/cX6onzVOBUgO6avUPqOPjgMJNdJa8YJVKp1l47DO1c3QQ/fHhoy6+25aLzleA7RI8PgPuJxsEDDwAFaBEB4ARgpAeTovNuQZhc5xV3/9rdvwYeA3au7wTuPtTdB7j7gC5duhQoLKkkrVtr9TSRcnXooauXmCdNgjFjYPDgwsVULhos17j75SW4/nRgb+AFM9sA2ByYDPQCTjGzqwir1+0J/K0E8UiFOfNM2HJL+MlPko5ERJrjr39dvc/36AGPPlo9Y99zNVpxaWYDgIuBHrnvj7NcrJndTegd39nMPgIuJXSYw93/BfwauNXMJhAS+YXu/oWZ3Ueotp9AqDl43N0fatqPJtXOPQyzOeaYpCMRkdXhHoa4tWzZ9M+usUb19Z7PitMyOYzQk34C0KRRhO5+dCPHP2HlBDq5+5cDP27KtUTqmjkzrBilHvQi5au2NiwMc8MNcOSRTfvs++/D7beHmrwNNihOfGkWp2Vyprs/6O5T3H1a9lH0yERWU21t2KoHvUj56tYN5s1rXke7u+6Cq66qvglusuKU4C81sxuBp4HF2Z3uPrJoUYkUgMbAi5S/NdcMneyamuDdw9KwAwfCRhsVJbTUi5PgTyD0bF+DlVX0DijBS6otXRo62PTokXQkIrI6MpmmT3YzfnyoxTvvvOLEVA7iJPgd3H3zokciUmCnnhoeIlLe+vaF++8Pq0K2bRvvM8OHh055hx1W3NjSLE6C/4+ZbeXu7xY9GhERkTq++90wL/3ixfET/OLFYaKrzp2LG1uaNbpcrJm9R5hlbgqhDd4AjzNMrtS0XKxkrVgBO+wAP/0pnHBC4+8XkcrjDpW+Dmm+5WLjlODzrQYnkkoffxxWoVq8uPH3ikj6ff11WB1yk00af++XgyTZ5QAAIABJREFUX8K661Z+cm9MnGFy3sBDJLXUg16ksuyyC5x2WuPvW7ECtt4azj238fdWujgl+EcICd0IS7j2At4H+hYxLpHVojHwIpUlk4GXX278fS+9FGrwdtih+DGlXaMleHff2t23ibZ9gG8DMf6ZRZIzcWIYPxunOk9E0q9vX5g6NUx6k09NTfjbP+igkoSVak1eY8vdxwE7FiEWkYLp2jUMj9EqciKVIZMJ23fzjOdatgzuuy8k97XWKk1caRZnsZnclowWwHbAJ0WLSKQAzjkn6QhEpJCyCf7tt2HHBoqYo0fD55/DoEElCyvV4rTBd8h5vozQJj+iOOGIrL7syM9q70ErUkl69YJrroE99mj4PQMGwM03V+/qcXU1Og6+nGgcvAB88AFsuy3ccUeY6EJEpFI1axy8md1Cw8Ph3N1PKkRwIoVWWxs64qy3XtKRiEghZee3qK8D3X/+A2PHwsknh052kr+K/uF69nUDzgFaFicckdWnMfAilenuu+GCC2DWLOjUadVj110HjzwCP/5xMrGlUYN9jN19RPYBvAEcAJwO/A7oXaL4RJqsthbWXhu6dEk6EhEppGxHu7oryy1cCA88EEbOtG5d+rjSKu8gIjPbwszuBB4CXgS2cvfr3H1JSaITaYaJE0PpXZ3sRCpL32h6tbprwz/2WGiWGzy49DGlWb42+HuB7YE/E6rllwNrW/St6e6zSxGgSFPtt19YJlJEKkvXrqF2rm6CHz481NgNHJhIWKmVrw1+B0Inu/OB86J92TKRo2p6SSnNQS1SmcxCNX1ugneHuXPhqKOgVZyB31WkwX8Od+9ZwjhECmLhQli6NNzli0jlufbaVf++zUIV/YoVycWUVprIUyrKE0/AOuuE4TIiUnn694feOfXHCxaEraal/ib9k0hFya4it+mmycYhIsXx1Vfwj3+EOem/+grWXx+GDk06qnRSi4VUlNra0NmmY8ekIxGRYli6FM48E/76V1h3XZg/P5Tq5ZtiJXgz2w3o4+63mFkXYC13n1Lc0ESaLjtETkQqU5cu4fH222Fmu169tPZ7QxqtojezS4ELgYuiXWsAdxYzKJHmqq1VghepdJkMPPccPPlkWDlOc17UL04b/KHAD4D5AO7+CauuMCcpMGwY9OwZOpr07BleVxt3uOQS+OEPk45ERIppjTVg0iRYvhxuu606v+/iiFNFv8Td3cwcwMzaFzkmaaJhw+DUU1f2Jp02LbwGGDIkubhKzQx+8pOkoxCRYho2LKz7nvXpp9X5fRdHnBL8PWZ2PdDRzE4BngJuKG5Y0hQXX7wyuWctWBD2V5OPPw49a5cvTzoSESmWiy+GJXUmS6/G77s4Gk3w7v4n4D5gBLA58Ct3/0exA5P4pk9v2v5KdcstYa7qxYuTjkREikXfd/HF6kXv7k8CTxY5Fmmm7t1DtXx9+6tJbW2Yq7pdu6QjEZFi0fddfHF60c8zs7l1Hh+a2Sgz03z0KXDRRd/sRdquHVx5ZTLxJEVD5EQq35VXfvMmvhq/7+KI0wb/N+ACYBOgK2HxmbuAGuDm4oUmcf34x6EnaffuIdH36BFmdqq2Die1tdCnT9JRiEgxDRkSvt969Kju77s44iT4H7j79e4+z93nuvtQYD93Hw6sW+T4pBGTJoWZnI45JlRbTZ8OHTpU33Kps2bB7NkqwYtUgyFDYOrUsMDM1KlK7g2Jk+AXmNlRZtYiehwFLIqOeRFjk0a4w9FHw157rdy30UYh2Q0fnlxcSWjXDh58EA4+OOlIRETSIU6CHwIcA3wOfBY9/5GZrQn8tIixSSMeegjGjFk5BhRCyf2oo+DRR2HOnORiK7U114SDDtIiMyIiWXGGyU1294PcvbO7d4meT3L3he7+YimClG9asSLM2rbZZnDssaseGzQojBN94IFkYkvCK6+EaStFRCRodJicmbUFTgL6Am2z+939xCLGJY0YMQLeegvuuCNM25hrp51Cx5Oamm8m/0r1t7+F2oxJk5KOREQkHeKMg78D+C+wH3AFocr+vWIGJY178knYcsvQBl+XGVx+ObRpU/q4kqIe9CIiq4qT4Ddz9yPN7GB3v83M7gJeKHZgkt/114de4w31lj/uuNLGkyT3MAZ+992TjkREJD3idLJbGm2/MrMMsA6wfvFCknyWLQtzrpvBeuvlf+9HH1VHb/oZM+Drr1WCFxHJFSfBDzWzdYFfAg8C7wK/L2pU0qDbbw89xd99t/H3Dh0alk6dMaP4cSWptjZsNQZeRGSlvAnezFoAc939S3d/3t17u/v67n59ieKTHEuWwBVXQL9+of29MYMGhd72991X/NiStOOO8MYbsPPOSUciIpIeeRO8u68AflaiWKQRN90UZqu74opvzj1fn759IZMJvekrWdu20L9/mMFPRESCOFX0T5nZ+WbWzcw6ZR9Fj0xWsXAh/OY3sOuu8N3vxv/coEHw0kvw4YfFiy1pt98OI0cmHYWISLrESfCD/r+9e4+SqjrzPv59uMtFMANRELmIwQQVTWzJvPFCDIkaA4boYJMha8RZGfUdjRmNDo43NLyaAGq8kDjBeMsKRA0aBZVJNJHwInkXgiAImm5uIhAFxBs20lye949dvWiavlR1nVOn6tTvs1av7jrn1DlP79Xw1Nln7/0AlwPzgSWZr8VxBiUHmz8/PEufNCm7u/c6lZXQpg0sXBhfbEmbMiUkeRER2a/FaXLuPrAQgUjzzj4b1q4NC9jk4nOfgy1bWh5xX6r27QuL23zzm0lHIiJSXLKpB9/ZzG40s+mZ158zs5HxhyZ1tm8P33NN7nXqkrunsDTQ22/Drl0aQS8i0lA2XfQPA7XAVzKvNwH/J7aI5AAffRSS109+0vpz1NaG5/ZTp0YXV7GomyKnOfAiIgfKJsEPcvcpZBa8cfcaIIenwJKPu+8O5V+/8Y3Wn6NDh7AQzIwZ0cVVLNatC991By8icqBsEnxtpjSsA5jZIGBXrFEJAO+/D3fdBaNHQ0VFfucaOzYUp8lmgZxS8m//Blu3Qu/eSUciIlJcsknwtwD/AxxlZjOAP6G58QVx552hpvutt+Z/rjFjwuj7tC1dawY9e+Y2s0BEpBxkUw/+j8D5wHjgt0CFu8+LNyyprYWHHgrT3IYOzf98vXvD8OEhwadpsN1VV6V/pT4RkdbIph78HGAmMNvdP4k/JIHw3Hz5cvj00+jO+cMfhille/YcXEO+FNXWwn33Qdeu8E//lHQ0IiLFJZtysXcQFrv5qZm9AjwGPOvuEaYeqW/nzrD8as+e0Z539Ohoz5e0detg716NoBcRaUw2XfR/cfd/B44GfglcCGyJO7ByNmFCWJJ2z57oz/3xx6FLOw3d9NXV4btG0IuIHCybQXZkRtFfAFwGnAI8GmdQ5WzDBvjlL0OhmHbZ9K/k6Mknw4C7V16J/tyFpjnwIiJNy2YluyeAN4CvAdMI8+J/EHdg5eq228Ld9Y03xnP+0aPD8/00jKb/5BPo2ze9y/CKiOQjmzv4BwlJ/TJ3fwn4ipn9POa4ytLatWHk/CWXtH5Z2pb06AHnnBMS/L598VyjUG66KfR4iIjIwbJ5Bv8HYKiZTTGz9cAk4M24AytH994buuWvvz7e61RWwqZN6agwp/nvIiKNazLBm9lgM5toZm8C9wFvA+buZ7r7fQWLsIxMngwvvAB9+sR7nfPOg0MOgT/9Kd7rxKmmBk4/HebMSToSEZHi1NwwrjeB/wuMdPfVAGZ2VUGiKkN790LHjnDaafFfq2vXMECtb9/4rxWX1athwQK44oqkIxERKU7NddGfD/wdeMnMHjCzEajITCxefx0GDYJFiwp3zVJO7rB/ipxG0IuINK7JBO/uT7v7WODzwEvAfwCfNbP7zeysQgVYDiZODIVljjmmsNe9/HK4+urCXjMqmiInItK8bAbZfeLuM919FNAXWApMiD2yMvHqq/DUU2FN9c98prDX/ugjePTRsORrqamuhiOOgG7dko5ERKQ4ZbXQTR13f9/dp7v7iLgCKjc33wyHHRYSfKFVVsL27fDii4W/dr569YIR+isUEWlSDGulSbaWLYPnnoPbb4fu3Qt//bPOCvPiH3sMzj238NfPx+TJSUcgIlLclOATdOKJMHs2nHlmMtfv0AHOPx9+97tQta5Tp2TiEBGR6OXURZ8LM3vIzLaY2etN7O9uZnPM7DUzW2lmF9fb18/M/mhmb5jZKjMbEFecSXEPi7SMGhWmrSXl4ovDynk1NcnFkKslS2DgQHj55aQjEREpXrEleOAR4Jxm9l8OrHL3E4GvAneaWYfMvl8DU939C8AwUla9zh1GjoSf/SzpSMK8+zvuKPwAv3z87W+wfn14vCAiIo2LLcG7+3xge3OHAN3MzICumWP3mNkQoJ27v5A5zw53L6H7y5a9+CI8/3zoIi8Ge/fCn/8cireUgurq0PsxaFDSkYiIFK847+BbMg34ArAZWAH80N33AYOBD8zsKTNbamZTzaxtUycxs0vMbLGZLd66dWthIs9DXaW4o46C738/6WiChQvDiPRSWfa1qgr69dOYARGR5iSZ4M8GlgF9gJOAaWZ2KGHg3+nANYTa80cD45s6SWbaXoW7V/Tq1Sv2oPP13HNhxbqbbgpL0xaDU08N69+XSgnZ6motcCMi0pIkE/zFwFMerAbWEVbN2wgsc/e17r4HeBr4UoJxRsY9rFp39NEwfnzS0ezXpg1ceGF4bPDhh0lH07Lhw8PgRBERaVqSCX4DMALAzA4HjgXWAq8APcys7nb8a8CqRCKMmBn86lfwwAPQvn3S0Rxo7Niwot3TTycdScumToUrr0w6ChGR4hbbPHgz+y1hdHxPM9sITATaA7j7fxPqyj9iZisIRWwmuPu2zHuvAf6UGYC3BHggrjgL7YtfTDqCxg0bBgMGwNy5cNFFSUfTtNpaaNs2fImISNNiS/Du/t0W9m8GGi1akxlBPzSOuJLy+OPh+fu0aXDooUlHczCzMLq/f/+kI2nezJlw6aVhoF2xxyoikiStZFcAe/aEQXWdOiW7qE1LSmHaWVUV7NsHRx6ZdCQiIsUtyWfwZeM3vwkjv3/84zCgrZjdfXdxd9FXV4dBiu300VREpFlFnm5KX20t3HornHwyfPvbSUfTsvffDx9I3nkn6UgaV1UFgwcnHYWISPFTgo/Zww+HZVUnTQrPuYtdZWXoAp81K+lIDrZvn+bAi4hkSwk+ZiNHwpQpcE5zq/IXkSFD4IQTQgnZYrN7N1x/fWhTERFpnrl70jFEpqKiwhcvXpx0GCXv9tvhhhtgw4awpK6IiBQnM1vi7hWN7dMdfExqamDMGFi6NOlIcldZCePGwa5dSUdyoHfeCV8p+kwqIhIbJfiY/OIX4Tl2qVRoq2/QoDDQ7phjko7kQFOnhjrwSvAiIi1Tgo/Bxx/D5Mlw9tmh3nqpWrUK3n036Sj2q6oKA+yKfaqhiEgx0H+VMbj3Xti2Lcx7L1WbN8Nxx8GDDyYdyX4aQS8ikj0l+Ih98AHccUeodjZsWNLRtF6fPqGMbLGMpt+zB9as0Rx4EZFsKcFHrEMHuO660r57r1NZCStWhK76pL31VkjyuoMXEcmOEnzEOneGCRPgpJOSjiR/Y8aE592PP550JPCZz8Ajj8CZZyYdiYhIaVCCj9A99xRPl3YUjjgChg+HZ55JOhI47LCwRv7AgUlHIiJSGpTgI/Luu2GVtWefTTqSaE2fDvPnJx0FLF4My5YlHYWISOlQTa6I/PSnYWGYiROTjiRaxTIX/oYb4L33QqIXEZGW6Q4+Ahs3wv33hy7kNA4C+/3v4TvfSXaBGVWRExHJjRJ8BG6/PVQ6u+mmpCOJx4cfwtNPw6JFyVx/164wij6NH55EROKiBB+Br34VbrkFBgxIOJCYjB4dpv8lNZp+zZrQe6A7eBGR7OkZfAQuvDDpCOLVo0cod/v442ERn0IvFVtdHb7rDl5EJHu6g89DdXUYXFeKBWVyNXZsWL52wYLCX3v4cHjxRTj++MJfW0SkVCnB5+HWW2HSpFBcJu1GjQp38W3bFv7aPXrAiBFhESEREcmOuuhbadUqmDkTrr02LAiTdl27wty5yVx75kzo2xfOOCOZ64uIlCLdwbfSxInQpUtI8OVkyxbYsKGw17z2Wnj44cJeU0Sk1CnBt8KyZTBrFlx1FfTsmXQ0hbN7Nxx7bGEL6ezYEZ79a4CdiEhulOBboU0bOO88uPrqpCMprPbtYeRIePJJqK0tzDVXrw7fNUVORCQ3SvCtMHRoKMDSo0fSkRReZWWoef/CC4W5XlVV+K47eBGR3CjB52jaNNi0KekoknPWWeGDTaGq5tXNgS+WNfFFREqFEnwOFiyAH/wgjOouVx06wAUXwOzZhemmv/ba0E3fpUv81xIRSRPzJCuIRKyiosIXx1hu7Mwz4Y03YO3a8p6TvW5dmA/fr1/SkYiIlDczW+LuFY3t0x18lv78Z5g3L9R8L+fkDjBwYOGS+7XXwksvFeZaIiJpogSfBXe48caw2MollyQdTXFYtCh01e/YEd813nsvrH2/dGl81xARSSsl+Czs3BlGcd98M3TqlHQ0xeHTT+Gpp2DOnPiuoSIzIiKtp6Vqs9C5Mzz6aNJRFJfTToM+fUKFue9+N55r1CV4zYEXEcmd7uBbsHChuogb06ZNKJM7d26YFx+HqqpwnYED4zm/iEiaKcE3Y98+uOyycIe6b1/S0RSfsWPDVLlnnonn/Nu2wdFHh6l5IiKSG3XRN2PWLFixIsx7b6OPQgcZNiyUkO3YMZ7z339/4ZbEFRFJGyX4RsyYEabDbdgQ1l/fuzfpiIqTWfwlZHX3LiLSOrovbWDGjDAVrq4k6u7dcOmlYbs0budOeOutaM+5ZQuMHg0vvxzteUVEyoUSfAM33AA1NQduq6kJ26VxZ5wB//qv0Z7zzTfDs/1PPon2vCIi5UIJvoG6O/dstwuce25Y5e+dd6I7p6rIiYjkRwm+gaaWYNW6602rrAyzDGbNiu6c1dXh+bvaXUSkdZTgG7jttoPXmu/cOWyXxg0ZAiecEG0J2aoqGDQoFLUREZHcKcE3MG4cTJ8O/fuHUeL9+4fX48YlHVlxGzs2DIh7++1oztelC5xySjTnEhEpRyoXK5H4+99h0yY4+eTwwUhEROLXXLlYzYOXSPTuHb5ERKQ4qIteIrN+fVhDYM2a/M7zl7+EnoBVqyIJS0SkLCnBS2TatoUHHggV5vKxciW8+ir06BFNXCIi5UgJXiJz1FFw6qn5j6avqgqD7NTlLyLSekrwEqnKylCgJ5/u9erqsMCNBuuJiLSeErxEasyYUHkvn276qiqtYCciki+NopdIHXFESPKdOrXu/e5h/vtpp0Ubl4hIuVGCl8jl8wzeDGbOjC4WEZFypS56icW+fbBxY+veJyIi+VOCl1iMHw+nnx663HNxzz1w+OHw0UexhCUiUjaU4CUWI0aEhW8WLcrtfVVVsHs3dOsWS1giImVDCV5iMXp0KPea6/P46moYPFhT5ERE8qUEL7Ho3h2++U144oncnqtripyISDSU4CU2Y8fC5s2wYEF2x9fUhHKzgwfHG5eISDlQgpfYjBoFs2fDl7+c3fG7dsFVV8Hw4fHGJSJSDlQPXkREpEQ1Vw9ed/ASqw8/hJtvhoULWz52+3b49NP4YxIRKQdK8BKrjh3h7rvhoYdaPvaaa2DQoPhjEhEpB0rwEqtOncKUuSefhNra5o+troZjjilMXCIiaacEL7EbOxY++ABeeKH54zRFTkQkOkrwEruvfx0OO6z5RW8+/BC2bNEUORGRqCjBS+w6dIBx46Bt26aPqa4O33UHLyISjVgTvJk9ZGZbzOz1JvZ3N7M5Zvaama00s4sb7D/UzDaa2bQ445T43XcfPPJI0/t794Y77wy14EVEJH9x38E/ApzTzP7LgVXufiLwVeBOM+tQb/8kYH5s0UnBbd3a+PYjj4Srr4a+fQsbj4hIWsWa4N19PrC9uUOAbmZmQNfMsXsAzOxk4HDgj3HGKIUzeTL07w87dhy8b/lyeOutwsckIpJWST+DnwZ8AdgMrAB+6O77zKwNcCdwTZLBSbS+8hXYuRPmzDl43/e/H75ERCQaSSf4s4FlQB/gJGCamR0K/DvwvLtvbOkEZnaJmS02s8Vbm+r/laJw6qmhK77haHr3/WViRUQkGkkn+IuBpzxYDawDPg/8L+AKM1sP3AH8i5n9tLETuPt0d69w94pevXoVKm5phTZt4MILYe7cMC++zrZt4bVG0IuIRCfpBL8BGAFgZocDxwJr3X2cu/dz9wGEbvpfu/t1yYUpURk7Fnbvhqef3r+tboqc7uBFRKLTLs6Tm9lvCaPje5rZRmAi0B7A3f+bMEr+ETNbARgwwd23xRmTJOuUU+DXv4Zzz92/raoqfNcdvIhIdFQuVhK3aVOoNjd6NLRvn3Q0IiKlQ+Vipajs2QO/+AU8+2x4feSRMGaMkruISJSU4KXg2raFe++Fu+4Kr2fNghUrko1JRCRtlOCl4MygshLmzYPNm+Gii7KrFy8iItlTgpdEVFaG+e/33AM1NRpBLyISNSV4ScSQIXDCCTBlSnitEfQiItFSgpfEfP7z+38ePx5mzEgsFBGR1FGCl0TMmAHPPbf/9aZNcMklSvIiIlFRgpdE3HBDePZeX01N2C4iIvlTgpdEbNiQ23YREcmNErwkol+/3LaLiEhulOAlEbfdBp07H7itc+ewXURE8qcEL4kYNw6mT4f+/cPCN/37h9fjxiUdmYhIOsRaTU6kOePGKaGLiMRFd/AiIiIppAQvIiKSQkrwIiIiKaQELyIikkJK8CIiIimkBC8iIpJCSvAiIiIppAQvIiKSQkrwIiIiKaQELyIikkJK8CIiIimkBC8iIpJCSvAiIiIppAQvIiKSQubuSccQGTPbCrwV4Sl7AtsiPF85UhvmT22YP7VhNNSO+Yu6Dfu7e6/GdqQqwUfNzBa7e0XScZQytWH+1Ib5UxtGQ+2Yv0K2obroRUREUkgJXkREJIWU4Js3PekAUkBtmD+1Yf7UhtFQO+avYG2oZ/AiIiIppDt4ERGRFFKCFxERSaGyTPBmdo6Z/c3MVpvZdY3sv8zMVpjZMjNbYGZD6u37r8z7/mZmZxc28uLS2nY0s2+Y2ZLMviVm9rXCR18c8vlbzOzvZ2Y7zOyawkVdXPL89zzUzP5qZiszx3QqbPTFIY9/y+3N7NHMvjfM7L8KH31xaKkN6x13gZm5mVXU2xZPXnH3svoC2gJrgKOBDsBrwJAGxxxa7+fzgP/J/Dwkc3xHYGDmPG2T/p1KsB2/CPTJ/Hw8sCnp36fU2rDetlnA74Brkv59Sq0NgXbAcuDEzOt/KMd/z3m24T8Dj2V+7gysBwYk/TsVYxtmjusGzAf+H1CR2RZbXinHO/hhwGp3X+vutcBjwLfrH+DuH9V72QWoG4n4bcIf8y53XweszpyvHLW6Hd19qbtvzmxfCRxiZh0LEHOxyedvETMbDawjtGG5yqcNzwKWu/trmePec/e9BYi52OTThg50MbN2wCFALVD/2HLRYhtmTAImA5/W2xZbXinHBH8k8Ha91xsz2w5gZpeb2RpgCnBlLu8tE/m0Y30XAK+6+65YoixurW5DM+sKTABuLUCcxSyfv8PBgJvZH8zsVTP7z9ijLU75tOEs4BPg78AG4A533x5vuEWpxTY0sy8BR7n7c7m+t7XKMcFnxd1/7u6DCP+J3ph0PKWquXY0s+MIn2YvTSK2UtFEG94C/MzddyQWWAlpog3bAacB4zLfv2NmIxIKseg10YbDgL1AH0L38o/M7OiEQixaZtYGuAv4USGvW44JfhNwVL3XfTPbmvIYMLqV702zfNoRM+sL/B74F3dfE0uExS+fNvwyMMXM1gP/AVxvZlfEEWSRy6cNNwLz3X2bu9cAzwNfiiXK4pZPG/4z4Xn8bnffArwMlONa9S21YTfCeKN5mX+z/wjMzgy0iy+vJD04IYHBEO2AtYRPm3WDIY5rcMzn6v08Clic+fk4DhwMsZYyHJQTQTv2yBx/ftK/R6m2YYNjbqF8B9nl83d4GPAqYXBYO+BF4FtJ/04l1oYTgIczP3cBVgFDk/6dirENGxw/j/2D7GLLK+2ayPup5e57Mnc6fyCMfHzI3Vea2Y8Jf7SzgSvM7OvAbuB94KLMe1ea2ROEP+I9wOVenoNy8mpH4ArgGOBmM7s5s+0sD3cAZSPPNhTy/vf8vpndBbxCGCz2vB/8fDT18vw7/DnwsJmtBIyQ7JcX/rdIVpZt2NR7Y8srWqpWREQkhcrxGbyIiEjqKcGLiIikkBK8iIhICinBi4iIpJASvIgkLlO05Ckze9nMBiQdj0gaKMGLSDE4kbCu/lTg/IRjEUkFJXiRlDOzvZkyn6+b2Rwz65Hj++fVlbY0s+dzfX8T5/yimT1Yb9PrwLmEynjP1DtuZGYusYjkSAleJP12uvtJ7n48sB24vLUncvdz3f2DCGK6Hri33uvdhJW8XiOsiFbnOWCUmXWO4JoiZUUJXqS8/JVMpSozG2ZmfzWzpWa20MyOzWw/xMweM7M3zOz3hDKgZPatN7OeZjbAzF6vt/0aM7sl8/OVZrbKzJab2WMNAzCzboTlTF+rt/kbwCLgQeB7dRs9rMQ1DxgZWQuIlImyW6pWpFyZWVtgBCGJArwJnJ5ZZvPrwO2E8r3/G6hx9y+Y2VDCeu25uA4Y6O67mujOryB0ydf3PeC3hGIlr5jZBN+/zOZi4HTgiRzjEClruoMXSb9DzGwZ8A5wOPBCZnt34HeZO/GfEYpeAJwB/AYgs654rmuLLwdmmNn3CGtrN9Qb2Fr3wsy6AMOBue6+jfDBY3i947cQypGKSA6U4EXSb6e7nwT0JxQEqXsGPwl4KfNsfhTQKYdz7uHA/z/qv/dbhCIP1a4HAAABNElEQVQkXyLcjTfsKdzZ4PjRhAqDVZlSmhWEGu31z70zh9hEBCV4kbLhoeb5lcCPMkm3O/vrTo+vd+h8Qp1vzOx4YGgjp3sX+KyZ/YOZdSTzjNzM2gBHuftLhFKi3YGuDd77BqGaYJ3vEUoHD3D3AcAA4FuZ8wIM5uAufRFpgRK8SBlx96WELvTvAlOAn5jZUg4cj3M/0NXM3gB+DCxp5Dy7M/sWEbr838zsagv8xsxWAEuBexuOunf3N4HuZtbNzD5LuGOfV2//J4QSrt/KbDqTMJpeRHKgcrEiUnBmdhXwsbv/qoXjDgdmuvuIwkQmkh66gxeRJNwP7MriuH7Aj2KORSSVdAcvIiKSQrqDFxERSSEleBERkRRSghcREUkhJXgREZEUUoIXERFJISV4ERGRFPr/qWxmYyDMlegAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "YIRhtXJCE3XQ",
        "outputId": "c7ef4a00-376a-4ab2-f3fc-d3caca1b5207"
      },
      "source": [
        "# Plot average total number of function evaluations per run\n",
        "\n",
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize = [8,8])\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        nfevList[start:stop], \n",
        "        '--o',\n",
        "        color = 'b')\n",
        "\n",
        "ax1.set_ylabel(\"Average Number of Energy Evaluations (total per run)\")\n",
        "ax1.set_xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"ADAPT-VQE: Energy Evaluations\\n\"\n",
        "title += \"{} shots, {}\".format(shots,name)\n",
        "\n",
        "ax1.set_title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'ADAPT-VQE: Energy Evaluations\\n8192 shots, H2')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63ftFvGI0Zh4",
        "outputId": "00bbb991-b4fd-436d-967b-8cec55fce587"
      },
      "source": [
        "print(np.average(adaptErrors))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.00038212834726167875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "YTmxFlhoHYU-",
        "outputId": "3e17b32a-291e-4f5f-c458-2eea96c84005"
      },
      "source": [
        "fileName = \"adapt_analysis2.pkl\"\n",
        "data = (totalShots, nfevList, adaptErrors, numberMeasuredObs)\n",
        "\n",
        "myFile = open(fileName, \"wb\")\n",
        "pickle.dump(data, myFile)\n",
        "myFile.close()\n",
        "\n",
        "files.download(fileName) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_eee9602c-78a3-4931-8bc2-517252c4db75\", \"adapt_analysis2.pkl\", 997)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "lEBCA4Fz1ZBd",
        "outputId": "37bf7862-e86f-465c-df05-43eb8a1508bc"
      },
      "source": [
        "# Upload from computer\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "  \n",
        "myFile = open(fn, \"rb\")\n",
        "(uccsdtotalShots, uccsdnfevList, uccsdErrors, uccsdnumberMeasuredObs) = pickle.load(myFile)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-03c5a41c-263f-4a4c-b880-2e9642d75207\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-03c5a41c-263f-4a4c-b880-2e9642d75207\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving adapt_analysis.pkl to adapt_analysis.pkl\n",
            "User uploaded file \"adapt_analysis.pkl\" with length 997 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "6jAwZ4ax1p1N",
        "outputId": "826129b1-872c-481b-8ae7-13a7fd64478c"
      },
      "source": [
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "adaptErrors = [np.abs(medianEnergy - energy) for (medianEnergy, energy) in \\\n",
        "               zip(medianEnergies, exactEnergies)][start:stop]\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize = [8,8])\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        adaptErrors[start:stop], \n",
        "        '--o',\n",
        "        color = 'b',\n",
        "        label = \"ADAPT-VQE\")\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        uccsdErrors[start:stop], \n",
        "        '--o',\n",
        "        color = 'orange',\n",
        "        label = \"UCCSD-VQE\")\n",
        "\n",
        "ax1.legend()\n",
        "\n",
        "l = ax1.axhspan(0, chemicalAccuracy, alpha=0.3, color = 'cornflowerblue')\n",
        "\n",
        "ax1.set_ylabel(\"Error (au)\")\n",
        "ax1.set_xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"UCCSD-VQE vs ADAPT-VQE: Error in the Energy ({name})\"\n",
        "\n",
        "ax1.set_title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'UCCSD-VQE vs ADAPT-VQE: Error in the Energy (H2)')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "Yfsdkt2k1p1Q",
        "outputId": "91c22189-ef9a-4795-9d28-04c5dc90ada4"
      },
      "source": [
        "# Plot average total number of measured observables per run\n",
        "\n",
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize = [8,8])\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        numberMeasuredObs[start:stop], \n",
        "        '--o',\n",
        "        color = 'b',\n",
        "        label = \"ADAPT-VQE\")\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        uccsdnumberMeasuredObs[start:stop], \n",
        "        '--o',\n",
        "        color = 'orange',\n",
        "        label = \"UCCSD-VQE\")\n",
        "\n",
        "ax1.legend()\n",
        "\n",
        "ax1.set_ylabel(\"Average Number of Measured Observables (total per run)\")\n",
        "ax1.set_xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"UCCSD-VQE vs ADAPT-VQE: Measured Observables ({name})\"\n",
        "\n",
        "ax1.set_title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'UCCSD-VQE vs ADAPT-VQE: Measured Observables (H2)')"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u3jzZvpxI3Hl5eYwZMybbYYiIiFQIM5udap+qykVERHKIEreIiEgOUeIWERHJITnRxp3MunXrmDt3LqtXr852KNVKnTp1aN26NbVqbcriTSIiUl5yNnHPnTuXhg0bkpeXR1hQRjLN3Vm8eDFz586lXbuSFhUTEZFMydmq8tWrV9O0aVMl7QpkZjRt2lS1HCIiWZSziRtQ0s4CXXMRkezK6cRdGbz88suYGdOmTQNg1qxZ1K1bl1133ZUdd9yRXr168fjjj2/0uvz8fE499dQNtp111lm0a9eO/Px8evTowWeffcaFF15Ifn4+Xbt2pW7duuTn55Ofn88LL4Sluj/88EP22mvDxXLWr19PixYtmDdvHu7OTTfdRKdOnejcuTP7778/EydOLHpuXl4eu+yyS9FxL7nkknK+QiIiUp5yto17Uw0bBtdeCz/8AG3awM03w4ABm3/c4cOH07t3b4YPH87gwYMB6NChA+PGjQPgu+++44QTTsDdGTRoEABTp06loKCAjz/+mJUrV1K/fv2i4912222cdNJJvPPOO5x//vlFSXbWrFkcffTRjB8/foPz77vvvsydO5fZs2fTtm1bAN5991122mkntttuO+69914+/fRTJkyYQL169XjnnXc49thjmTx5ctF5P/jgA5o1a7b5F0NERDKuWpS4hw2D886D2bPBPXw977ywfXOsWLGCTz75hEceeYRnn02+SmX79u254447uPvuu4u2DR8+nIEDB3LooYfyyiuvJH3dfvvtx4wZM0qNoUaNGvTr12+D8z/77LP0798fgCFDhnDvvfdSr149AA499FD23Xdfhm3uLy8iIllRZRL3AQds/LjvvrDv6qth1aoNn79qFVx6afh+0aKNX5uOV155hcMPP5zOnTvTtGlTxo4dm/R5PXr0KKpKB3juuec49dRT6d+/P8OHD0/6mtdee41ddtklrTj69+9flLjXrFnDm2++yYknnsiyZctYuXIl7du33+D5PXv2ZMqUKUU/9+nTp6iq/M4770zrnCIikh3Voqp87tzk2xcv3rzjDh8+nEuj7H/qqacyfPhwLrrooo2eF7/m+ZgxY2jWrBlt2rShVatWnH322SxZsoQmTZoAcOWVV3LTTTfRvHlzHnnkkbTi6NmzJytWrGD69OlMnTqVPfbYgyZNmrBs2bK0Xq+qchGR3FFlEvfo0an3tWkTqscTRU3CNGtW8uuTWbJkCe+//z6TJk3CzCgoKMDMuPDCCzd67rhx49hxxx2BkOynTZtGbLWzZcuW8eKLL/K73/0OKG7jLklBQQG77bYbAMceeyw33nhjUal76tSpRdXkjRo1on79+nz33XcblLrHjh3LoYceumm/sIiIVApVpqq8JDffDFETb5F69cL2snrhhRcYOHAgs2fPZtasWcyZM4d27doxZ86cDZ43a9YsrrjiCi6++GIKCwsZMWIEkyZNYtasWcyaNYtXXnklZXV5KjVr1mT8+PGMHz+eG2+8EQjV5U8//TTvv/8+ffv2LXrulVdeySWXXMJvv/0GhI5rkydPLvXmQEREKqdqkbgHDIChQ0MJ2yx8HTp083qVDx8+nOOPP36DbSeeeCK33norM2fOLBoO1q9fPy655BIGDRrExx9/TKtWrdhuu+2KXrPffvsxZcoU5s+fX/ZggB133JH69etz4IEHbtBL/eKLL6ZXr15069aNvLw8zjjjDEaNGkWdOnWKnhPfxn3GGWdsVhwiVdGwYZCXBzVqhK+50LczF2OW9Fh8+2tl1bNnT09cj3vq1KlF1c+SnhUrVnD88cez++67c8stt5T5OLr2Up3ERqXEd3CtV2/zb/4zKRdjlg2Z2Vh375lsX5Vp45bSNWjQgFGjRmU7DJGccu21yUelXHQRzJkDXbpArPLtzjthzZoNn7vLLnDUUeH7IUPCkNR4PXvCwQfD+vVw++0bn3/vvWG//cI540aVFtl/f9hrL1i6FB54oPg8yWK+9lol7qpAJW7ZZLr2Ul3MmRM6t5bkpJPg+efD91tvHRJovEGD4NFHw/dbbAEFBRvuv+QSuOsuWL0a6tbd+PjXXgs33QQLF8I222y8/9Zb4S9/ge++gw4dSo7VDAoLS36OVA4qcYuIbIKvv4bbboNnngnJLln5Zvvt4ZtvQhtyTLKuKjVrFn+/YkXq/bVrQ9SHNOn+Zs2S798i+hTPyyve37lzuOlIVNpNiOQGJW4Rkci0aXD55fDmm6FN+MILoV07uOaajduLb70V4vp4Ahv/nKik/Wabt79GjeL9t966cRs3wIknlhyf5AYlbhGp1goK4JdfQom2Th0YNw5uvBH+8Ado2jQ8p1mzzKx1kCmx2GIxt2oFtWqFKvnu3UGDR3KbEreIVEurV8OTT4YOYR07hlJ2Xl5IdFskfDIOGFC5E3UyiTEvXx460Z15JixZAn/8Y/Zik81TLcZxZ8qsWbPYeeedN9h2ww03cHvUNfT2229nhx12ID8/n913350nn3wSgHXr1vGXv/yFTp060aNHD/baay/eeustAB599FF22WUXunXrxs4771y0CElsyc/u3bvTuXNnzjjjDOYmmct10KBBPPjggxtse/nllzniiCMAmDt3Ln379qVTp060b9+eiy66iDVRN9jRo0fTuHHjojHd+fn5vPvuu+V4xUSy75dfQlVyXh6cfz40ahQ6kMUkJu2qomFDeOMNOOEEuOyy4g51knuqT+L+fhi8nAfP1Ahfv8/sbAQPPPAAo0aN4ssvv2T8+PG89957RXOWX3/99cyfP5+vv/6ar776ipdffpnly5czd+5cbr75Zj755BMmTpzI559/Trdu3YqOedtttzFhwgSmT5/OrrvuyoEHHsjatWs3OG/8giMxsdXC3J0TTjiB4447jm+//ZZvv/2W3377jT//+c9Fz913332LZmUbP348Bx98cAavkkjFe+CB0Gadnw/vvQf/+x+cfHK2o6oYtWvDiBFhWFncBIuSY6pH4v5+GHx5HqyaDXj4+uV5GU3et9xyC/fffz+NGjUCwrzhZ555JqtWreKhhx7innvuoXbt2gC0aNGCfv36sWDBAho2bEiDBg2AMO66Xbt2Gx3bzLjsssvYdttti0rqMQcddBDTpk0rmolt5cqVvPvuuxx33HG8//771KlTp2hd8Jo1a3LnnXfy5JNPsiJZd1eRKuDrr+Gss+CFF8LPv/89jB8PI0fCgQeGTl/VSc2acPHFsOWWYYjZlVdCwv2/VHJVJ3G/e8DGj2+idT0nXA0FCd0rC1bB2Ghdz9WLNn7tZli1ahXLly/faDlNgBkzZtCmTZuihB6ve/futGjRgnbt2jFo0CBee+21Es+TuFwohGR84oknMmLECCAsD3rAAQfQqFEjJk+eXLQ4SUyjRo3Iy8srWvv7448/3qCqfObMmZv0u4tUBu7w0Udw9NFhApTnn4cffwz7tt46dNCScPNy++1wzDHJh6pJ5VR1EndJVqVY13Pt5q3raSlu1cs6qU3NmjUZOXIkL7zwAp07d+ayyy7jhhtuSPn8VOeJry6PVZOnK7GqvENpMzqIVEIDB4YZxb78Ev7+99DhLFqBV+IMHAiPPALvvguHHBI6rUnlV3W6YRw8OvW+em2iavLE7dG6nnWalfz6FJo2bcovv/yywbYlS5aw22670aBBg42W0wTo2LEjP/zwA8uWLUta6jYzevXqRa9evTjkkEMYNGhQyuQ9btw4DjroIF566SUGDx4MwMMPP8zee+/N/PnzmTBhAp9++mlREu/atSsvxOoLI8uWLeOnn36iS5cufPHFF5t8DUQqg9Wr4amnoH9/aNAg9J7eZ59QRZ5sNjIpdvbZ0KQJnHpqmFr17bfD8DGpvDJW4jazOmb2pZlNMLPJZjY42v64mX1vZuOjR36mYijS/WaombCuZ816YftmaNCgAS1btuT9998HQtIeOXIkvXv35uqrr+bCCy9k2bJlQFjg48knn6RevXqcc845XHrppUUdyxYuXMjzzz/PvHnz+Oqrr4qOP378eNrGFg2P4+7cfffdzJ8/n8MPP5zjjz++qITcs2dPzIxTTjmFM888kyOOOKJoJbCDDjqIVatWFfVuLygo4PLLL+eiiy6ibjX/dNNKSrnpl1/gllvC3+y88+Cll8L2E0+ECy5Q0k7XccfBW2+Fcey1amU7GimVu2fkARjQIPq+FvAFsCfwOHDSphxrt91280RTpkzZaFuJvnva/aW27sMsfP3u6U17fQqTJ0/2Aw44wLt37+7du3f3p58Oxy0sLPQhQ4Z4586dfaeddvL8/Hx/6qmn3N19zZo1fuWVV3qHDh18p5128l69evnIkSN91qxZ3qdPH+/SpYt3797dDz74YJ8xY4a7u5955pmel5fn3bp1844dO/rpp5/uc+bMSRnXuHHjHPC33nprg+0//PCDH3PMMd6xY0dv3Lixn3feeUX7PvjgA2/UqFHR79K9e3d//vnnNzr2Jl/7Su7pp93r1XMPLaPhUa9e2C6V09q17n/6k3uDBuHvdfjh7u+/715YmO3IcltBQfi6dq37tGnZjaW6A8Z4ipxYIYuMmFk94BPggujxuru/UPKrimmRkcz49NNP6d+/Py+99BI9evRI+3VV7drn5cHsJC0pbdvCrFkVHY2U5KefYNttw/d9+kDr1nDFFepsVt4uvxwefhheey1Un0vFK2mRkYx2TjOzmmY2HlgAjHL3WCPqzWY20czuNLPaKV57npmNMbMxCxcuzGSY1dbee+/N7NmzNylpV0U//LBp26ViucOHH4alMdu1C8kbQoeqp55S0s6EP/4RttsODjssJG+pXDKauN29wN3zgdZALzPbGbga2AHYHWgCXJXitUPdvae792zevHkmw5RqLlVHnNat4dBDQ7tp4lKMknkFBfDii7DnnnDAAWGilGuvLW63jl91S8rX9tvDxx+HoXTHHx+mhpXKo0KGg7n7UuAD4HB3nx9V4a8BHgN6VUQMIqkkW+O4Xr0wUceMGWGKyK5d4aGHQu9lqRizZoUZzRYvhvvvD80Z110HjRtnO7LqoVmzMLPcAQeECVsWLcp2RBKTyV7lzc1sq+j7usAhwDQzaxltM+A44OuynqMi2udlQ1Xxmj/zTGjTa9s2zKLVti0MHRqmxfzmG3juuTDE6LzzQnu4PsAyI9ZD/Pzzw88dOsAnn8D06eEmSj3EK15sfvOPPgqJHJKvTS4VK5Ml7pbAB2Y2EfgfoY37dWCYmU0CJgHNgJvKcvA6deqwePHiKplIKit3Z/HixUXDy3JZQQEMHx4+hLp0CbNHzZoFhYXha2xVpS22gH79YMyYUPoYNKj4A+zZZyHJOi9SisShd3ffDX/6U6ievfZamDMH1q0Lz917b1WJZ1vt2sX9CO68Myx3qqajYtkYSlohvco3V7Je5evWrWPu3LmsVt1lhapTpw6tW7emVg4P9nQPpeeHH4ZRo6As66j8+iu0bBkSzIABYb7nnXYq/1irmmHDwrVflTADsVm4juohXrldc01YWe2UU0K795ZbZjui7Er2/1yvXqix29xlYEvqVZ6ziVukrP7yFxgyJJTubipTfU8wezbccUe4AVi1KvR6vv122GGH8ou1qtl+++S1FK1aqfYiV9x+e7hRPfRQ+M9/oH79bEeUPZkcSpq14WAilc2QIeFxwQVhDuvN0bYt3HVXGDZ2440wdmyYeQpCO3hh4ebHWxXMnBlucPbfP3VynjevYmOSsrviCnj00eL5zdevz3ZE2TFlSvKkDZkfSqrELdXGd9+FXsn9+8O995bfco5Nm8L114e22by8sO2MM2DHHUNpvLq15hQWhg5lMaedFjr/LV2aukd4mzYVE5uUj0GDwlC9AQNCP5DqZvjw0DSW6jMk0//PStxSbbRvH3rHPv546EhS3uI/wAYNCj1yf/e7MGnIP/4REldV9dtv8Prrob2vVavQTh1bJvLee8NN04QJ8O9/hzbAePXqwc2bt2yAZMFxx8GFF4bvR48OIzCqqsJCeOUV+OCD8POhh8LgwXDffVn6f041F2pleiSbq1wkXW+/7Z5kyvWMKyx0f+8998MOC/Np33JLxcdQEZ57rniu94YN3fv1C/O8r1qV/PlPPyysraEAACAASURBVO3etq27WfiqOeFz29q17u3auTdv7v7VV9mOpnytXu3+8MPuXbqE/+8TTtj4OZn6fybbc5VvLnVOk7L67LPQa3yHHcLazNkaWjR+fGgT33preOGFUDrNxZ7oM2bAq6+G0seVV8LRR8PkyaEk3bdvmKyjdtJJjKUqmz49lEKXLq0685s/9ljoRf/TT7DrrnDVVWHVuYpqGlDnNKmWJk6EI48Mcy6/+WZ2xwPn54ekDaGD1vPPw847wzHHhKklK/P982+/hQ+wnXaCTp2K26tjnZJ22ilUGR52mJJ2ddWlS5gsJza/+auvZjuisvnxx+KhXevWhSlfR40KHU9POaXytOcrcUuVNHNm+ACpXz+88Vq0yHZExf74x+Ke6J9/HkonZ5yR7aiKxdqrY/NT16kTZo/bdlv417+K26uPOy67cUrlEj+/ea4l7ilT4OyzQ3+Uxx4L2373O3jnnVBjV14dWctLJbl/EClfL7wAa9eGD5JYT+/KJNYT/fLL4YkniudLX7EizMh2+unFQ8sqwqJFYWrLV16Bt98OpY5OnWDgwPChNXWqJtuQ0jVrBu+/X/y/u2wZNGqU3ZhK8skn8M9/hur9unXD1LpHHRX2VbZkHU9t3FIlucP8+aHqLpc89VQofbdoAZdeGj5IYlXs5W3mzFDCqFEjTGN5//2hR/ixx6q9WjbfokWwxx5hOOCNN1bORLjPPqE3/MUXh/dAbDrjykBt3FItrFwZVvKaODF8SORa0oZQ0n7vvTCc6pprwnjQyy8vnrt7cxQWhqr5q68O7dIdO4Y52CFU348ZE8aiq71aysPWW8NBB4XZCS+4IPvzm69ZA488EjqaLVgQtj31VJhE5a9/rVxJuzRK3FIlrFkT1g1+5RX4/vtsR1N2ZnDggaG6ety4UPodNw5iU8P//HPZjvv11+FGZq+94LbbiturY80InTvDbrtVzlKR5KaaNeHBB8MUww8+GErea9dWfBy//hpmS8zLg3PPDf/jP/0U9rVvv/E47FygNm7JeQUFoaQ6alSYirFv32xHVD7y88MiBrHe2z/9FKq2DzoI/vznUDq+9trQ0a1NmzDpw4ABoYry9ddDB6FevcIHZ8eOoZPNkUfCEUdkrvpdJJ5ZWJSkWbMwVer224e5zivKL7+E98yvv4bhak8/HW6Mc/0GVW3cktPiV/q64w647LJsR5Q5v/4alsC8++6QnGvU2HA+9Fq1Qgni22/D9lat4JJLQpIXybbnnw9Js2nTzJ5nypTQQe6ii8LPd94Z+mvsumtmz1vetDqYVFmrV4fq5F69Nm+lr1yyalUouSxZsvG+WrVCCbtvX+jRI/dLFlL1rFkT1l+/5ppwc1ke3OG//y3uIV6/fqiJatKkfI6fDUrcUiWtXx8mRFi3LnytTkmqRo3kk7aYaVUyqdwmToTevUNSfeed0L9ic0yeHMZcf/ZZKM1ffHGYQz2XOpslo17lUuU89FB48y9dGkqZ1SlpQ+rVh7TKllR23bqFxTpWrgzv4XHjNv0Ya9YUL6nZvHloRrr33lDK/tvfcj9pl0aJW3LOiBFw/vnh7rp+/WxHkx0336xVtiR37bZbmPykbt2wTvtHH6X3uqVLi3uIn3Za2LbNNmHUxIUX5mYP8bJQ4pac8vbboQf5PvuEzi6xYVLVzYABMHRoWLjELHwdOjRsF8kFXbqEdulddoGttir5uT/+GBa1adMm9OHo1i1M6hJrLqpuNW5K3JXcsGHh7rJGjfB12LBsR5Q9n30WJljp2jV0QKkud9epDBgAs2aFNu1Zs5S0Jfe0bh1K3t26hST8978n/7x76aUwauToo+Grr8IN/EEHVb+EHaNx3JXYsGFhqFNstZrZs8PPUD0/pFu0CAtyPP546XfoIpIbYsn3oovCrH0xs2eHhT8gfD3qqDAmW9SrvFLbfvuwBGSitm1DCau6WLAgdDapofohkSqrbdvQuSzZ9ur0eRejXuU5ZObMUCW0//7JkzaEO9HffqvYuLJl3jzYc88wX7eIVF1z5iTfniyZV3dK3JXE2rVhYYmOHUOS+uUXaNw49fPbtg0TjuRAhUmZLVkSpilcuBD69892NCKSSRrimD4l7ixYvRrefDMMaTrjjLBtyy3DtHz/+hd8912YpODf/04+5Oe668JMYWPGFLcPLV5cob9Cxq1YEebV/vbbsHBIr17ZjkhEMklDHNOnzmkVaOTIMHHI22+HyQcaNAjTdbqHBHzXXRs+P9YBLdlCElC81OOMGWGZxpNOCkMm8vMr7nfKlP794X//gxdfDPMbi0jVVtrnnRRT57QMmjkzlBbPPRcaNYJ//APuuSck6759oU+f8lnz+Oefwxy9Q4eGkuqhh4aFJXJ5FZx33w3t27EaCRGR6kRzlVeQwsJQSnz11ZCwJ08O2994I1T7rlkTqsQzlUx/+QUeeCCU3H/5JXT22GabzJwrE9zh88/DmtEiItWZepVn0OrVocQLMHVq6AE9ZEhImLH26iOPDPtr185sCXjrreHqq8PQiVGjipP2mWeG8ZGVvSf6VVfB3nuH5C0iIskpcZfBokXwxBNw4olhfHFsveOuXcM0nAsWhPVgL700OxMG1KkTJioBWL4cpk8P8/i2bRtmJqqMHdmGDIHbboM//AH22CPb0YiIVF5K3Jto4MAwg9dZZ4WS4cCB4XsIpemTTqpca8A2bBimCv3ww9Az+69/DZ0+Ro/OdmTFhg4N8w/37x/6AORqu7yISEVQr/IUYu3Vr7wS5tL94AOoWRN23TXModu3L/TokRuzeZmFEvh++4VVdO69F3pGLSejR4cq9u7dsxPb11/D738fmhOeeCI3rqeISDapc1qCSZNCYnvtNZg/PyTr/faDZ56BbbetkBAqVK9e4QblsMNClX+fPhVf4h0+PNwIVfdFQ0REYtQ5LZJspa1Ye/WUKeE5P/0UknTv3vDUU8Xt1VUxaQO88w7ccguMHx9W29l999CxLdM++wzGjg3f9++vpC0ikq5qU+JOXGkLQgIvLAzfDx4c2n/Xr4eCgvIZX51LVq8ONyq33RbWuT311DBJjFn5J9WJE8Nc7B06hNK+2rRFRDakEjdhNp74pA0haTduHJLH9deHbVtsUf2SNoSe6L/7XRjSdvLJYdvdd4ee6DfeWH490WfODBPE1K8fZkVT0hYR2TSlJm4z28bMjjezC83sbDPrZWY5l/BTrTCzbFnoqKUEEtSsGR4Q5k7fc0/4299CT/RLL9285fXmzYNDDgm1GqNGhZsCERHZNCkTsJn1MbO3gTeAI4CWQFfgOmCSmQ02s0YVE+bm08ozm26vvUInvUmTQin8vvvCOOuyuu22sNLXW2/BjjuWX5wiItVJyjZuM7sNuMfdNyqrmtkWwNFATXd/MbMhZq6Nu169MIZYk9inZ+7c0O7dpUtYE/z3v4crrkh/TvR160JVfLdumY9VRCSXlamN292vTJa0o33r3f3likja5WXAgJCk27YNSaZtWyXtTdW6dUjaEJbbHDcODj44NDU891yoAk+0Zk2oYv/5Z6hVS0lbRGRzldqr3MxqAycCecRN2OLuN2Y0sji5sshIdRPriX777fDNNyGpT5wYpn2NLc1Xp06YI33EiOJObyIiUrKSStzpzJz2CvArMBZYU56BSW6L9UQ/++ywItq0aSFpxzdJ/PZbKGmvXZvdWEVEqop0Stxfu/vOFRRPUipx5468vND+naht283rkS4iUp1s7jjuT81sl3KOSaqoVMPuUm0XEZFNk07i7g2MNbPpZjbRzCaZ2cRMBya5ScPuREQyK5027iMyHoVUGTffnHzY3c03Zy8mEZGqJJ0St6d4iGxEw+5ERDIrnRL3G4REbUAdoB0wHdgpg3FJDhswQIlaRCRTSk3c7r5BxzQz6wFsxsSXIiIiUlabvFiIu38F7JGBWERERKQUpZa4zexPcT/WAHoA8zIWkYiIiKSUTht3w7jv1xPavHNmjnIREZGqpMTEbWY1gYbufkUFxSMiIiIlKLGN290LgH0qKBYREREpRTpV5ePN7FXgeWBlbKO7/ydjUYmIiEhS6STuOsBi4MC4bQ4ocYuIiFSwdMZxD6qIQERERKR0mzyOW0RERLJHiVtERCSHlJi4zayGmfWrqGBERESkZKUNBysE/lyWA5tZHTP70swmmNlkMxucsP9uM1tRlmOLiIhUV+lUlb9rZleY2fZm1iT2SON1a4AD3b07kA8cbmZ7AphZT2DrsoctIiJSPaUzHOyU6OuFcdscaF/Si9zdgViJulb08Gg2ttuA04DjNylaERGRai6d4WDtynrwKEmPBToC/3b3L8zsUuBVd59vZmU9tIiISLVUalW5mdUzs+vMbGj0cyczOzqdg7t7gbvnA62BXma2H3AycE8a5z3PzMaY2ZiFCxemczoREZEqL5027seAtcDe0c8/AjdtykncfSnwAdCHUPqeYWazgHpmNiPFa4a6e09379m8efNNOZ2IiEiVlU7i7uDu/wTWAbj7KqDUOm4za25mW0Xf1wUOAca6+7bunufuecAqd+9Y5uhFRESqmXQ6p62NEq8DmFkHQo/x0rQEnojauWsAI9z99TJHKiIiImkl7r8BI4HtzWwYYZnPs0p7kbtPBHYt5TkN0ji/iIiIRNLpVT7KzL4C9iRUkV/q7osyHpmIiIhsJJ0SN8D+QG9CdXkt4KWMRSQiIiIppTMc7D7g98Ak4GvgfDP7d6YDExERkY2lU+I+ENgxmgkNM3sCmJzRqERERCSpdIaDzQDaxP28fbRNREREKlg6Je6GwFQz+5LQxt0LGGNmrwK4+7EZjE9ERETipJO4/5rxKERERCQt6QwH+7AiAhEREZHSpdPGLSIiIpWEEreIiEgOUeIWERHJISnbuM1sEtHCIom7AHf3bhmLSkRERJIqqXPa0RUWhYiIiKQlZeJ299kVGYiIiIiULp25yvc0s/+Z2QozW2tmBWa2rCKCExERkQ2l0zntXqA/8C1QFzgX0CIjIiIiWZBWr3J3nwHUdPcCd38MODyzYYmIiEgy6Ux5usrMtgTGm9k/gfloGJmIiEhWpJOAB0bPuwhYSVgd7IRMBiUiIiLJpZO4j3P31e6+zN0Hu/uf0FAxERGRrEgncZ+ZZNtZ5RyHiIiIpKGkmdP6A6cB7WJrb0caAUsyHZiIiIhsrKTOaZ8SOqI1A/4vbvtyYGImgxIREZHkSps5bTawl5m1AHaPdk119/UVEZyIiIhsKJ2Z004GvgROBvoBX5jZSZkOTERERDaWzjju64Dd3X0BgJk1B94FXshkYCIiIrKxdHqV14gl7cjiNF8nIiIi5SydEvdIM3sbGB79fArwVuZCEhERkVRKTdzufqWZnQD0jjYNdfeXMhuWiIiIJFNq4jazIe5+FfCfJNtERESkAqXTVn1Ikm1HlHcgIiIiUrqSZk67APgD0N7M4idcaQj8N9OBiYiIyMZKqip/htAJ7VbgL3Hbl7u7pjwVERHJgpISd4G7zwL6p3qCmTVw9xXlHpWIiIgkVVIb9ytm9n9mtp+Z1Y9tNLP2ZnZONETs8MyHKCIiIjElzVV+kJkdCZwP7GNmWwPrgenAG8CZ7v5TxYQpIiIiUMpwMHd/E3izgmIRERGRUmjqUhERkRyixC0iIpJDlLhFRERySDrrcXcws9rR9weY2SVmtlXmQxMREZFE6ZS4XwQKzKwjMBTYnjA5i4iIiFSwdBJ3obuvB44H7nH3K4GWmQ1LREREkkknca8zs/7AmcDr0bZamQtJREREUkkncQ8C9gJudvfvzawd8FRmwxIREZFkSl2P292nmNlVQJvo5++BIZkOTERERDaWTq/yY4DxwMjo53wzezXTgYmIiMjG0qkqvwHoBSwFcPfxQPsMxiQiIiIppNU5zd1/TdhWmIlgREREpGSltnEDk83sNKCmmXUCLgE+zWxYIiIikkw6Je6LgZ2ANcBwYBnwx0wGJSIiIsml06t8FXBt9BAREZEsSpm4zew1wFPtd/djMxKRiIiIpFRSifv2CotCRERE0pIycbv7h7HvzWxLYAdCCXy6u6+tgNhEREQkQalt3GZ2FPAAMBMwoJ2Zne/ub2U6OBEREdlQOsPB/g/o4+4zIKzPDbwBKHGLiIhUsHSGgy2PJe3Id8DyDMUjIiIiJSipV/kJ0bdjzOxNYAShjftk4H8VEJuIiIgkKKmq/Ji4738G9o++XwjUzVhEIiIiklJJvcoHVWQgIiIiUrp0epXXAc4hTHtaJ7bd3c9O43UfAbWj87zg7n8zs0eAnoQe6t8AZ7n7ijL/BiIiItVIOp3TngK2BQ4DPgRak17ntDXAge7eHcgHDjezPYHL3L27u3cDfgAuKlPkIiIi1VA6ibuju18PrHT3J4CjgD1Ke5EHsZJ0rejh7r4MwMyM0FaeclpVERER2VBa63FHX5ea2c5AY2CbdA5uZjXNbDywABjl7l9E2x8DfiLMxnZPiteeZ2ZjzGzMwoUL0zmdiIhIlZdO4h5qZlsD1wOvAlOAIekc3N0L3D2fUL3eK0r8sY5v2wFTgVNSvHaou/d0957NmzdP53QiIiJVXjqJ+zF3/8XdP3T39u6+jbs/uCkncfelwAfA4XHbCoBngRM3KWIREZFqLJ3E/b2ZDTWzg6J26bSYWXMz2yr6vi5wCDDdzDpG2ww4FphWhrhFRESqpXTmKt8BOBq4EHg0Wqf7WXf/pJTXtQSeMLOahBuEEYQ5zj82s0aE4WATgAvKGryIiEh1U2ridvdVhKQ7ImrrvoswLKxmKa+bCOyaZNc+ZYhTRERESK+qHDPb38zuA8YSJmHpl9GoREREJKl0Zk6bBYwjlLqvdPeVmQ5KREREkisxcUft04+6+40VFI+IiIiUoMSq8mjI1tEVFIuIiIiUIp1e5f81s3uB54CianJ3/ypjUYmIiEhS6STu/OhrfHW5AweWfzgiIiJSknSGg/WpiEBERESkdKUOBzOzFmb2iJm9Ff3c1czOyXxoIiIikiidcdyPA28TFgUB+Ab4Y6YCEhERkdTSSdzN3H0EUAjg7uuBgoxGJSIiIkmlk7hXmllTQoc0zGxP4NeMRiUiIiJJpdOr/E+Edbg7mNl/gebASRmNSkRERJJKp1f5V2a2P9CFsKLXdHdfl/HIREREZCPp9Co/Gajr7pOB44DnzKxHxiMTERGRjaTTxn29uy83s97AQcAjwP2ZDUtERESSSSdxx3qQHwU85O5vAFtmLiQRERFJJZ3E/aOZPQicArxpZrXTfJ2IiIiUs3QScD/CBCyHuftSoAlwZUajEhERkaRKTdzuvgqYBRxhZhcDLd39nUwHJiIiIhtLp1f5X4EngKZAM+AxM7su04GJiIjIxtKZgGUA0N3dVwOY2T+A8cBNmQxMRERENpZOG/c8oE7cz7WBHzMTjoiIiJQkZYnbzO4hzE/+KzDZzEZFPx8CfFkx4YmIiEi8kqrKx0RfxwIvxW0fnbFoREREpEQpE7e7PwFgZnWAjtHmGbG2bhEREal4Kdu4zWwLM/snMJfQq/xJYI6Z/dPMalVUgCIiIlKspM5ptxEmW2nn7ru5ew+gA7AVcHtFBCciItXI98Pg5Tx4pkb4+v2wbEdUKZXUxn000NndPbbB3ZeZ2QXANODSTAcnIiLVxPfD4MvzoGBV+HnV7PAzQLsB2YurEiqpxO3xSTtuYwGhd7mIiEj5mHBtcdKOKVgVtssGSkrcU8zsjMSNZnY6ocQtIiJSPlb9sGnbq7GSqsovBP5jZmcThoQB9ATqAsdnOjAREakmZjxEyorcem0qNJRcUNJwsB+BPczsQGCnaPOb7v5ehUQmIiJV35QhMP4v0LgbrPgWCn4r3mc1ofvN2YutkkpndbD33f2e6KGkLSLVj3o7Z8a8t0PSbnsaHDEGej0E9doCBrUagxdArYbZjrLSsST9zyqdnj17+pgxY0p/oohIeUvs7QxQsx70GqrezpvLHX54HtqcBJZQjixYC2/vDmsWwtHToFaj7MSYJWY21t17JtuXziIjIiLVl3o7l6+C1fD5OfDrNDCDtv02TtoANbeEPR+Fbn+HLVTqjpfOsp4iItWXejuXn3XL4MO+sGA0NO8NjXco+flNdgsPAC9MnuCroZKmPF1uZstSPSoySBGRrEnVq1m9nTfN6gXwbh9Y+Ans9TR0GJT+a2ePgJG7wfqVmYsvh6RM3O7e0N0bAXcBfwFaAa2Bq4B/VUx4IiJZ1v1mqLHlxtu33hUKCyo+nly06kcYtS8smwr7vbLpfQPqbgu/jIcJ12cmvhyTTr3Dse5+n7svd/dl7n4/0DfTgYmIVArtBsCWzaBmHcCg3vbQfD/48WWYqHbutGzZBBrtAH3egVZHbvrrt9kPOl0A0/8Fiz4v//hyTDpt3CvNbADwLGGEfH9A9RUiUj2sXgCr50H+P6DrVcXbv3sSWh4avncPHa1kQ0vGQYN2sOVWsP8rm3es/H/Aj6/DF+fA4V9BzdrlE2MOSqfEfRrQD/g5epwcbRMRqfoWfhK+Nt93w+3tzwhVuIUFMPoImHZXSOAS/PQuvLsvjLm4fI5XqxHs/gD8OgXmvVk+x8xRpZa43X0WqhoXkepqwUehmrxJ0iG1YaavGrXhqz/Cok9hj4c1acgPL8Knp0GjLrDrP8vvuK2OhCMnwla7lN8xc1CpJW4z62xm75nZ19HP3czsusyHJiJSCaxZDM32CeOKk6nVAPZ7GfKHwJwXwqQhSydXbIyVyYyH4L/9oOnucPCHULdl+R4/lrSXfg2F68v32Dkinaryh4CrgXUA7j4RODWTQYmIVBp7PwV93i75OWbQ9c9w4HuwdmlIXF5YMfFVJutWwNc3wbaHhY5oW26dmfMsnQxv5cO0OzNz/Eounc5p9dz9S9uw40X1vM0RkeqpRs30ntfiADhiHKxeGCYLKVgLeNXvSOUOeKh9OOTjUMquUStz52vcFVodA5P+Cq2Pg0adMneuSiidEvciM+tAtOaamZ0EzM9oVCIilcHXN8EHR2xa6bluS9i6W/h+3JVh/PLK2ZmJrzIoXA9fnA1jLgoJvH6bzCZtCDUcPf8d+hZ8eW61q91IJ3FfCDwI7GBmPwJ/BH6f0ahERCqD+SNh7S9ln2qzRR9YPh3e2hV+rII9oQtWwycnwXePQ50WFXvuettBjztC58EZD1bsubOsxP9GM6sJ/MHdDwaaAzu4e293r8K3jyIiwPrfYPGXYfKPstr+ODh8bJge9cOjwsxfVWW2tXXL4IPDYe4rsNvdsMvfKn4se/tB0PIIWLe8Ys+bZSW2cbt7gZn1jr7XpCsiUn0s/hIK121e4gZo2BEO/SxUJU/7P2h3Ru63ybrD6KPCLGZ7PZ295U3N4IDXq93iI+l0ThtnZq8CzxM3Y5q7/ydjUYmIZNuCjwCD5vts/rG2qAt7PgI7XQMNO4Rty2eEpJ6LzKDrX8L3rY7KcixR0v7xTShcA9sfn914KkA6ibsOsBg4MG6bA0rcIlJ1NWgPHc4p3yFNsaQ9azh8dgbseht0uTR3pkv9dSos+SqUsLOdsOO5w+SbYNn0cKNVZ5tsR5RR5jkwRV/Pnj19zJgx2Q5DRKR8rF0Kn58V2ofbnBzNttYo21GVbNGXYWrXmnXhmOmwRf1sR7ShX6eEToCtj4fez2Y7ms1mZmPdPel0faWWuM3sMaKhYPHc/exyiE1EpPJZ+2soBWcqmW65Fez7Eky9DSZcA79MgH1fhK12zsz5NtdP78JHx4We4weOqnxJG8LY7p2vh4nXw9z+0LrqztSdTov+68Ab0eM9oBGwIpNBiYhk1cyH4YUmYbrTTImfbW39clj+TebOtTl+eDF0RGvQHg75JHytrLpeBVt1g/9dEHq9V1HpLDLyYvzPZjYc+CRjEYmIZNuCj0KCqt008+dqsT8c/U2YdQzg5w+g2d6VZ7a15dPDvOP7v5a5KUzLS41asOejoQZji6q70EtZ+tB3Aqp2y7+IVF9eCAs/3ngZz0yKJe1Vc+GDw2BUb1gxq+LOn8gdVv0Yvu96NRz4fuVP2jFNdoMOZ4cajSo6o1o6q4MtN7NlsQfwGnBVaa8TEclJv04Js6Vt7vjtsqjXGvYZAcu/hZE9sjPbmhfCuCvgjZ1hxfchAaZaGa0ym/0cvNUD1le9KUhKTdzu3tDdG8U9OidWn4uIVBkLPgpft6nAEne8xNnWJv614s5duB4+Pxum3QHtTof6bSvu3OWtbktYOgEmVL1VqNMpce9jZvWj7083szvMLIf/miIiJWh5GOz+ANRvl70YGnYIs621H0TZWjTLIDbv+PdPwC43hGlMc3lGsm32g05/gOl3hRneqpBSx3Gb2USgO9ANeBx4GOjn7vtnPLqIxnGLSLXlHqqrf/4wdL5qvndmzjPp7zDpb9DzHuh8YWbOUdHWLQtV/rUawuFfVZ4Of2koaRx3OrdT6z1k977Ave7+b6DU7npmVsfMvjSzCWY22cwGR9uHmdl0M/vazB41swyv/yYikqbffoJZz4Zx3JWFWUjeE66Bd/eHaf+K1r8uZ13/DH3erjpJG8I4/F4Phn4L86rO6mzpJO7lZnY1cDrwhpnVANJJtmuAA929O5APHG5mewLDgB2AXYC6wLllilxEpLzNHwmf9g+9uysTMzjgjTDN6FeXwX9PKZ9xyitnw0fHw5oloTTa8pDNP2Zls90RcOSkKjWHeTqJ+xRCEj7H3X8CWgO3lfYiD2ITtdSKHu7ub0b7HPgyOp6ISPYt+CiM3W68Y7Yj2VhstrX8ITDnRRi5O6xeUPbj/ToF3tkHfh4NK74rtzArpdiMdEu/Dh3wclw6vcp/cvc73P3j6Ocf3P3JdA5uZjXNbDywABjl7l/E7asFDARGpnjteWY2xszGLFy4MJ3TiYhsngUfQ/PelbdTVtFsa+9Diz5Qu3nZjrPoCxi1L3gBHPwhnH1RQAAAIABJREFUNE3alFq1LJ0Mb+XDtDuzHclmS6dX+Z5m9j8zW2Fma82swMzSagBy9wJ3zyeUqnuZWfxEvPcBH8VuCJK8dqi793T3ns2bl/GfU0QkXavmwYoZ0DwL47c3VYv9odcDIZGv+B7G/in0Ck/Hgo/g/YNCCf6QT2DrbpmNtbJo3BVaHQOT/grLKun0smlK57byXqA/8C3FbdL3bcpJ3H0p8AFwOICZ/Q1oDvxpU44jIpIxi6MhQ9mYeGVzzHsLpt+Z/mxrDTvBtgeHpB1bZrQ6MIPd74MateGLc3N6VrW06oPcfQZQMypBP0aUgEtiZs3NbKvo+7rAIcA0MzsXOAzo757DV05EqpbWx8Ox38PW+dmOZNN0/gPs9zIsnxHNtvZG8ufNGxnad+u2DM+v27Ji46wM6raEHneEKW1nPJjtaMqs1EVGgFVmtiUw3sz+CcwnvYTfEnjCzGpGzx/h7q+b2XpgNvCZhcXj/+PuN5YtfBGRcmIGDfKyHUXZtO4bZlv75CT48Gjo/UKoOp9wLaz6AWo1hnVLw6QqXS7OdrTZ1X5Q6Ny3bnm2IymzdBL3QELivQi4DNgeOLG0F7n7RGDXJNvTOaeISMVZswTGXAQ7Xh4WqchFDTvAIZ/ClH/Aul9hzMVQsCrsW7cUrGZI4NWdWVjprLJ2QExDOr3KZwMGtHT3we7+p6jqXESkalj4CcwenvsLUmxRF7oNhkk3FiftGC+o2HnPK7NY0v7xDZjzn+zGUgbp9Co/BhhPNGzLzPLN7NVMByYiUmEWfgw1toSmvbIdSflY9cOmba+O3GHyzfDF7zZvPHwWpFNXcAPQC1gK4O7jgSzOvi8iUs4WfBSSds062Y6kfNRrs2nbqyMz2ONhWL8iNCvkkHQS9zp3Txy3nYGJckVEsmDdCljyVe4NAytJ95uhZr0Nt9WsF7ZLscZdYefr4YcRMOflbEeTtnQS92QzOw2oaWadzOwe4NMMxyUiUjF++xEa7QDbVNiCh5nXbgD0Ggr12gIWvvYaGrbLhrpeBVt1gzF/KJ/53ytAOst61gOuBQ4ldFJ7G/i7u6c5Tc/m07KeIiKSMUvGwi8Tof1ZoQq9EihpWc9Sh2a5+ypC4r62vAMTEcm62HrXUn012a14GKAXVvqhYikTd2k9x9392PIPR0SkAhWsgVfawi6DodP52Y5Gsm32czD5Vjj0v7BF/WxHk1JJJe69gDnAcOALQjW5iEjVsWQsrP4Z6myT7UikMqi7HSydABOug90q7ypiJdUHbAtcA+wM3EWYa3yRu3/o7h9WRHAiIhm14KPwtXnv7MYhlcM2+0KnP8D0u2DhZ9mOJqWUiTtaUGSku58J7AnMAEab2UUVFp2ISCYt+CgMCaqjpYMlkv8PqNcavjgnNKVUQiW2wJtZbTM7AXgauBC4G3ipIgITEcmowgJY9N/cWH9bKk6thmHo3LKpMO/NbEeTVEmd054kVJO/CQx2968rLCoRkf9v787j46rr/Y+/PkmbtulOmxZK95XS2gKWArLvIrIIXq9aUVxAZFVBQVAQFK+gwgVEfiCLC/VyWWVXtmLLIqWlO7Sl+96ELnRJtySf3x/fyW1ammTSzJkzZ+b9fDzmMTNnZs5553TST8453yVq1Vtg8GXQ/bi4k0iu6fFZ+NxM6DQs7iR7VG8/bjOrAWpH3K/7JgPc3TtEnO3/qB+3iIjEYv0M6DAUirI7sWVD/bgbusZd5O7tU7cOdW7ts1m0RUQisX4mVFU2/j4pXOtnwosHw+zb4k6yi9zuZS4iEgV3ePUEePd7cSeRXNZxGOx/Jsy4ATbMjTvN/1HhFpHCs2EObKuAsqPjTiK5zAwOvRuKWsHEC8Koajmg3sJtZq2yGUREJGsqJoT7fJoRTKLRZj845LbQdXDevXGnARo+4n4bwMz+mqUsIiLZUT4eWneH9oPiTiJJ0P+b0ON0qNrc+HuzoKFmciWp6Tw/k+rLvQt3fzK6WCIiEaqYEE6Ta3IRSYcZHPtsznxfGircFwFjgE7AGbu95oAKt4gkjzsc+WjWu/dIwtUW7eXPQc126PWJ49msqfeb6+5vAG+Y2SR3fyCLmUREomMGXUfHnUKSyB1m/So0bux6JLTpHkuMdFqV/9XMLjezx1O3y8ysZeTJRESisPhRWJ6bQ1lKjjODwx6Aqk0w+fLYYqRTuP8AfDp1/wfgEOCeKEOJiERmxg3w4R/iTiFJ1XEoDL8eljwKS/8eS4R0LvIc6u4j6zx/zcymRRVIRCQyW8thw2zof37cSSTJDvwxLHksDOCz7wnQMruDiaZTuKvNbIC7zwcws/5AdbSxREQiUJ7qv60ZwaQ5ilrC4Q/C+unQon3WN59O4f4RMM7MFhAmGOkDfDPSVCIiUaiYAMVtYJ9Px51Ekm6fQ8Jt4ViYdi1ULoXS3jDyZug3JtJNN1q43f1VMxsEDEktmuPuuTm7uIhIQ9bPhK6HQ3FJ3EkkHywcC+98K3QPA6hcDBMvDI8jLN5pdWRMFerpkaUQEcmGE16GHR/HnULyxbTrdhbtWtWVYXmEhVuTjIhI4TCDkk5xp5B8UbmkacszpMHCbUGvSBOIiGTD7NvhnQvCIBoimVDau2nLM6TBwu3uDmikAhFJviWPh2vcOTLetOSBkTdDcemuy4pLw/IIpXOq/D0zOzTSFCIiUaraAmvf1TSekln9xsDo+6C0D2DhfvR98bcqBw4DxpjZYmBzSIe7+4hIk4mIZMqad6BmB3Q7Ou4kkm/6jYm8UO8uncJ9auQpRESiVD4BMCg7Mu4kIs3W6Klyd18M9AJOSD2uTOdzIiI5o2UH6Hk2lHSOO4lIszVagM3sBuBq4CepRS2Bh6MMJSKSUQdcAcc8GXcKkYxI58j5C8CZhOvbuPsKIPuDs4qI7I3qbeA1cacQyZh0Cvf2VLcwBzCzttFGEhHJoLl3wZPdYMeGuJOIZEQ6hftRM7sX6GRmFwCvAH+MNpaISIaUT4CSfbI+9aJIVNJpnPZb4HHgCWAwcL273xV1MElZOBb+3hf+VhTuF46NO5FIcnhNmBFM/bclj6Q1yQgwA2hDOF0+I7o4souFY8NMM9WV4XmWZp4RyRsfz4Lt66BM/bclf6TTqvw7wETgHOCLwL/N7FtRBxPCDDO1RbtW7cwzItK48vHhXkfckkfSOeL+EXCwu68BMLMuwFvAg1EGE2KbeUYkb3Q5DIbfAG37xp1EJGPSKdxrgI11nm9MLZOolfYOp8f3tFxEGtdlVLiJ5JF6C7eZ/TD1cB7wjpk9TbjGfRYwPQvZZOTNu17jBihqFfnMMyJ5YWsFbJgDXUZDcUncaUQypqFr3O1Tt/nA30n14waeBhZGnEsAyj4DB/+mzswzveCwB9QwTSQdy5+DV46GTfPiTiKSUfUecbv7jdkMInsw9Roofx3OXgZFLcMyr4GPJkLX0bFGE8l5FROgVRfoMDTuJCIZlU6r8lFm9pSZvWdm02tv2QhX0DYvhaVPQN/zdhZtgNm3w8tHwPpZ8WUTSYLy8aEbmFncSUQyKp2R08YCDwHnAmfUuUmUPrwHcBh86a7L+58PLdqHo3ER2bPKFbBpvrqBSV5Kp3BXuPsz7r7Q3RfX3iJPVsiqtsD8+2D/s6Bd311fa9UFhv0EVjy3s4+qiOyqYkK418ArkofSKdw3mNn9ZvYVMzun9hZ5skJWMQG2rYUhV+z59cGXQ5v9YcrV4L7n94gUsp5nwUn/gs4HxZ1EJOPS6cf9TeAAwjzctXPjOaDJbaOy3ylw1qLQinxPWrSBETfBzF/ClhVQun9W44nkvOLWOk0ueSudwn2ouw+JPIkENVVQ1ALaNjLISr9vQN8xUNwqO7lEkmLbWnj/FhjwbegwOO40IhmXzqnyt8zswMiTSPDGF+Ht8xt/X1FxKNpVW2DtlMhjiSRGxRvwwa2wdXXcSUQikU7hPhyYamZzUl3BZqg7WEQ2zodlz9R/inxP/v0NeP002LEpulwiSVI+Poww2OXQuJOIRCKdU+WfjTyFBHPvAiuGQd9L/zNDfgBLHoPZt8Gnro8um0hSVExIDXPaOu4kIpFI54jb67lJJu3YAPMfhN5fgtIe6X+u7AjodQ588BvYWh5dPpEk2LEJ1k5WwzTJa+kU7ueB51L3rwILgBejDFWQFvwJqjbW3wWsISN/BdVbYMZNGY8lkiib5kPLjuq/LXmt0VPl7v6pus/N7BDg4sgSFao+X4Hi0r0bg7zDEBhwAWx4H2qqQ8M1kULUeSScW6HxDSSvpXPEvQt3fw84rLH3mVlrM5toZtPMbJaZ3ZhafqmZzTMzN7Oue5E5P7Uug4Hf2fvPf/p2OOFVFW0RK9LvgeS1Ro+468zLDaHQHwKsSGPd24AT3H2TmbUE3jCzF4E3CafeX2963Dz13lXQ/XjY//S9X0dtQ5wtK8N1vg6DMpNNJCmqt8E/D4Nh10KfL8WdRiQy6Rxxt69za0W41n1WYx/yoLaPUsvUzd19irsv2ru4eejj92H272D9tOavq6YaXjoS3v2uThVK4Vk7KfweFZXEnUQkUulc497rebnNrBiYDAwE7nb3d/Z2XXlrzp3haHnAhc1fV1ExHPBDmHwZrPwH9Dit+esUSYraSXfKjoo3h0jE6i3cZvYQ9Xf7cnf/dmMrd/dq4CAz6wQ8ZWbD3X1mOsHM7ELgQoDevRsZ/jOptq2FhX8JQ5e2ztDl/oEXwpz/hqlXw76n6FqfFI7yCdDxwMz9LonkqIZOldd2Aat7mw6cCJzalI24+3pgHE0YzMXd73P3Ue4+qqysrCmbS47594duXHvTBaw+xSUw8mZYPwMWjc3cekVyWU11GOq0TP23Jf/VW7jd/YnaGzAFOI3QDezXQP/GVmxmZakjbcysDXAyMDsjqfNFaa/QjavTpxp/b1P0/o8wctTHaZ3cEEm+qg3Q6wvNa+ApkhDmDTRiMrMDgJ8CBwO/AR5296q0Vmw2AvgzUEz4A+FRd7/JzC4HfgzsC5QDL7h7g/2gRo0a5ZMmTUpns1KrequGfBQRSSgzm+zuo/b0WkPXuB8DPg38DvgBUA10MDMA3H1tQxt19+mEgr/78juBO9MNn7eWPAE9Pgst2kaz/tqivW5amCK0pHM02xHJBVtWQevukPr/SSSfNXSN+1DAgKuAd4BJhBbik1OPZW+tfS9M3znvj9Fup3IZ/OPTMOvX0W5HJE7u8MIImHRp3ElEsqLeI25375vFHIVlzh3hSLv/+dFup7Qn9Plq2N7gS6FtE6YLFUmKDXNgWwXsc0jcSUSyoslDnkozbVkNix+BfudDSafotzfyF4DDDE35KXmqorb/tlqUS2FQ4c62efdCzXYYcll2tte2TzjaXvDn0EVMJN+UTwjXt9sPjDuJSFaocGfbuqmw32lhRq9sGXYttNlPhVvyU/n4MP+2GqZJgWh0yFMAMzsKGOTuD5lZGdDO3RdGGy1PHfMkVG3O7jZbdYEzF0Bxq+xuVyRqXgOH3BZm1xMpEI0ecZvZDcDVwE9Si1oCD0cZKi+5hyFOIbouYA0pbhUyrHpNE5BI/rAi6H1uOOIWKRDpnCr/AnAmsBnA3VcQZgqTpvjoLXiqB6weF1+G5c/CayfC0ifiyyCSSStfgrVT4k4hklXpFO7tHoZXcwAzi+FwMQ/MuQOK24ShSOPS43ToOAymXQs1O+LLIZIpky+H6T+LO4VIVqVTuB81s3uBTmZ2AfAKEPHIIXlm81JY+iQM/E48p8lrFRXDQb+GjR+GCU5EkmxreejDrdPkUmAaLdzu/lvgceAJYAhwvbvfFXWwvPLh3YCHbllx63E6lB0NM26EHZviTiOy98onhHsVbikwabUqd/eXgZcjzpKfqreHo9ueZ4c+1XEzg4NvhQlfhI1zNdqUJFf5+HD5qbO+w1JYGi3cZraR1PXtOj4mjFd+pbsviCJY3igugZPfCt1WckXXw1Pdw0riTpI7Fo6FaddB5RIo7R3mNO83Ju5U0pCP3oKuR+h7LAUnnSPu/waWAX8jTDryZWAA8B7wIHBcVOHyRofBcSf4pOKScDbgo7eh+7Fxp4nXwrEw8UKorgzPKxeH56DinctOeh22VsSdQiTr0mmcdqa73+vuG919g7vfB5zq7v8LaK7IhqweB+PPgS0r406yZzNvhNdOgg0fxp0kXtOu21m0a1VXhuWSu1q0hXZ9404hknXpFO5KM/uSmRWlbl8CtqZe00geDZl9O1S8kbtzYQ++DIpKYHqBF6jKJU1bLvGbdz/MuCnuFCKxSKdwjwHOA8qB1anHXzOzNkAONJPOURvnw/LnYNBFUNw67jR71mZfGHolLHkMPpoYd5r4tOmx5+WlvbObQ9K34CFY+Y+4U4jEIp3uYAvc/Qx37+ruZanH89x9i7u/kY2QiTT3LihqAYO+F3eShg39EbQqg6lXF+ZQqBvnQfW2Ty4vKgkN1CT3VG2Bte+Gbo0iBSidscpbm9klZvYHM3uw9paNcIm1YwPMfxB6fynMypXLWraH4deHzNvXxZ0mu9ZNg5ePAnMYfiOU9gEsFO3iUuh1TtwJZU/WvBNG/lP/bSlQ6Zwq/yuwL3Aq8C+gJ7AxylCJ5w4H/AAO+GHcSdIz6CL47LvQap+4k2TPlpXwyrFQ1BJOmgAjroezF8FXa+D4l2DHeph7Z9wpZU/KxwMGZUfGnUQkFukU7oHu/jNgs7v/GTgdOCzaWAlX0hFG3JicwU2KWoRZlratKZxr3W32gxG/gJPfhI5Dd32t+7FhhLm5v9eY7rmoZhuUHQUlneJOIhKLdPpx1/7Ptd7MhgOrgG7RRUq4irdg62rY/8wwNniSvPnlMPbzGXNzt0Fdcy36H2g/CLqMgiGX1f++Q++GotbhiFxyy8ibC7M9hkhKOkfc95lZZ+CnwDPA+8AtkaZKshk3wKTLgBwaKS1dw66FyqXhSDMfzbkL3voqvJ/G17dtH2jTPRSIqs3RZ5P01BZss3hziMSowcJtZkXABndf5+7j3b2/u3dz93uzlC9Z1s+CVa/A4IuTeaTW/XjY7zSY9av8aqjmDtN/HqaA7Hk2fOavaX6uJsxf/u7FkcaTJph9O7wwEqoqG3+vSJ5qsHC7ew3w4yxlSb65d4VTzAMujDvJ3jvo17B9Pcz6ddxJMsNrwhmQmTdC/2/BUY+lfxnAimCfQ2HhX0MLdIlf+etQvRValMadRCQ26Zwqf8XMrjKzXma2T+0t8mRJs20tLPwL9B0DrbvGnWbvdR4B/c4Lp8zz4Tqi14QR0Ib+CA67PzTEa4ph14RGUFOviSafpM9rwlSe3dR/WwpbOv+L/Wfq/pI6yxzon/k4CbZ5YRiBa8gVcSdpvsMeaHqByzVVm8OtdTc4+sm9/3lKOsOw62DKVbDqNdj3hMzmlPStnxm66ZWp/7YUtkb/N3P3ftkIknj7fDq0xrZ0TmLkuNoit2EOWDG0Hxhvnqbavg5e/3w4pXrqxOb/ETL4EphzZ+jXrcIdn4oJ4V4Dr0iBS2fktFIz+6mZ3Zd6PsjMPh99tATZtDAMw5gPRbtW9dYwqtjk78edpGm2rISXj4G1k0Ir+Ux0yStuDce9AEc+0vx1yd5rPwgGXhRa/IsUsHQqzUPAduAzqefLgV9GliiJ3j4PXj0u7hSZVdwahl4FK56H1f+KO016Ns6Hl44Mly2Oex56n5u5dXcaFvZJ9VYNyhKX/U6B0feoK5gUvHQK9wB3v5XUQCzuXgnoN6fW2slQ8Sb0+XLcSTJv8OVQ2hOm/jgZDdXe+Q5UbYATXoN9T8r8+resgmeHwIf/L/PrloZtXw+bF8edQiQnpFO4t6em8HQAMxsA7GE6pQI1+w5o0S50Nco3LdrAp26CNRNh6RNxp2ncEX8J4453HR3N+lt3h/YDYOZNYVIWyZ6lT8HTfeHj2XEnEYldOoX758A/gF5mNhZ4FfXtDrasgiWPQP/zw/jk+ajf16HzIbl7tLP8efj3N6GmGtr2+uS445lkBgfdAts+gg9+G9125JMqxkOrrtBhSNxJRGKXTqvyl8xsMnA44RT5Fe7+UeTJkmDpk+F65+AGxrxOuqJiOPWd3OwetvBh+Pf50PkgqNqYnUknuhwapmv94HdhrvVcn7Y1X5SPD/Nv6/q2SFqtyp8FTgFed/fnVLTrGPQ9+NwM6DA47iTRqi3aq16DHZvizVJrzp2hUWC3Y+DEcdmdKWrkzVCzHRb8KXvbLGSVy2HTAg28IpKSzqny3wJHA++b2eNm9kUzy9Opo5rAPfz132l43Emy4+P3w7jds38Xd5IwHOvkK8K448e9AC3bZ3f77QeG+csP1GhqWVGu/tsidTVauN39X+5+MWGktHuBLwHlUQfLae7w6vH5M553OjoeCL3OhQ9+A1tWx5ul7MhwtqMp445nWueDwh9uaqQWvX1PhM/8DTqNjDuJSE5Ia8SQVKvyc4GLgEOBP0cZKud99BaU/yu7p2dzwchfhX7MM3+R/W1Xb4dlz4bH3Y6GQ/8Q/3X38gnwVE/46N/x5sh3rcug71fi//cWyRHpXON+FPgAOAH4PaFfdx63xkrDnDugZacwGUch6TAYBl4I8+6FDR9mb7tVm2H8WTD+TFg/I3vbbUzng8MsVVMS0s89ibavC/OoV66IO4lIzkjniPsBQrG+yN3HAZ8xs7sjzpW7Ni8NrckHXgAt2sadJvuGXx8GZdk0Lzvb27YWXjsZVr0Eo/8InT6Vne2mo2U7+NQNYQzt5c/FnSY/lY8P86hvmh93EpGckc417n8CI8zsVjNbBPwCKNxRED68G/Aw8UQharMvnDEPepwW/bYqV8Arx4bR6Y56DAZ+J/ptNtWA74QxtKddE/qSS2aVT4CiVqEbnogADRRuMxtsZjeY2WzgLmApYO5+vLvflbWEuabf12HUHwp7ooOi4lCklj0T7Sni1a+GgV+OexF6nRPddpqjqGW49v/x+6Hdg2RW+XjoMjq+RogiOaihI+7ZhOvan3f3o1LFWocUHQ+EQd+NO0X8Fj0crjuveDHz667eGu77nRemSs31qTR7nQufm577OZNmxyZY9566gYnspqHCfQ6wEhhnZn80sxMp5MlF3OG9K2HtlLiT5IY+X4F2A2Dq1Zk9RVw+AZ4ZsLOldpt9M7fuqJjtvPa+/eN4s+ST9TMAV+EW2U29hdvd/+7uXwYOAMYB3we6mdk9ZnZKtgLmjNWvwuzbYP20uJPkhuKS1CnimeHoOxOWPwfjTgkDqrTpkZl1ZtOc38Mz/WHbmriT5IeyI+DctdDt2LiTiOSUdBqnbXb3v7n7GUBPYApwdeTJcs3sO6BVWX5O37m3ev8H7HMoTP/ZztPbe2vhX2H82dBxWJjhq23vzGTMpu7HwY71MOtXcSfJHyUdobhV3ClEckpaA7DUcvd17n6fu58YVaCctHEerHgeBl2kRjJ1mcHBt4RpTTcv3fv1rHoF3v56atzx18KAG0nUaTj0+wbM/T1sWhR3mmSr3gbjToOVL8edRCTnNKlwF6y5vw+jNg36XtxJck/341MTrQza+3V0Ox4O/l1q3PEOmcsWhxE3gRWFsxCy99a8Cyv/AdWVcScRyTkq3OloVQYDL9IUjvUpKg5jdq/4Z/qf8RqYfkOY+amoGIb+MD/OZpT2hCFXhEF6thb2kP7NUpGaWKTsqHhziOQgDf6bjuHXxZ0g9029BuY/GLpvNXZ9uno7/PsbsPgRKOkMB3w/Oxmz5cCfwKBLoHW3uJMkV/n40N6hVZe4k4jkHB1xN8RrYOVL4V4aVjvF5fTrG35f7bjjix+Bg27Jv6INoUFV216hC+H29XGnSZ6aKqh4U93AROqhwt2Q5c/DuFPDCGHSsLa9YfClsPAv9U8Esn3dznHHD7sfDvxxdjNm2zvfgtdO0h9+TbVtTRjidN+T4k4ikpNUuBsy5w5osz/sf3rcSZJh2LXQsmM4bV6fmh1h3PEB385errh0Oz6Ms77ksbiTJEub7nDiq7k7zK1IzFS467N+Zhh0ZfAlYTxqaVyrfWDYT8CroWrLzuWbFoV+3iWd4dR3Cuc/5L5joNMImHZtuK4v6dG+EmmQCnd95twZWjkPvDDuJMky9Croex48NxT+VgRP9oAXRsC7F4fXrYC+ckXF4Tr+pgVhDnNpnDs83Sf0OBCRPSqg/0WboKY6dEfp+zW1am2qRf8DEy+EysWAw9aVULUJOgyNO1k89js19HWf/8doZ1LLFxtmw9ZVyRw5TyRL1B1sT4qKw6AiVZviTpI8067bw6AZDnPvhgN/FEukWJnB4X+Ckn3CY2lY+fhwX6YW5SL1UeHeXU1VaAVcXAIlneJOkzyVS5q2vBDUHj3W7IDqLckfHS5KFROg9b7QfmDcSURylk6V727ZU+Ea24YP406STKX1nOKsb3mhqNkBLx4CU/K8C1xzlY+Hbkfr7IRIA1S4dzfnDmhRCu36x50kmUbeDMWluy4rLg3LC1lRyzB72Pz7YcOcuNPkpppqOOBK6P/NuJOI5DQV7rrWTg4jNg2+LFznlqbrNwZG3welfQAL96PvC8sL3fCfQXGb0D1MPqmoGA64AnqcFncSkZwW2TVuM2sNjAdapbbzuLvfYGb9gEeALsBk4Dx3z42Om7PvCFNU6i/+5uk3RoV6T1p3g6E/hhnXQ8XbUHZE3Ilyy5p3w4BHpT3iTiKS06I84t4GnODuI4GDgM+a2eHALcDt7j4QWAfkxhBaW8thySOhaJd0jDuN5KsDfgCtu8OCB+NOknvePg8mfjfuFCI5L7Ijbnd3oLY/VcvUzYETgK+mlv8Z+DlwT1Q50taqDI77h65tS7RatoOTJkD7AXEnyS1bVodr//1z4+94kVwW6TVuMys2s6lAOfAyMB9Y7+5VqbcsA/av57MXmtkkM5tUUVERZczaDcK+J0C7vtFvSwpbh0FhBLnt60P3Q9k5/3a3o+PNIZIAkRbWkQlfAAANqUlEQVRud69294OAnsBo4IAmfPY+dx/l7qPKysoiywiESSAmXQFVuw8cIhKRjfPhmQFhNjWB8gmh90HnQ+JOIpLzstKq3N3XA+OAI4BOZlZ7ir4nsDwbGerlDrN+DateDi1+RbKhXX9oPzjMX64/GKFiPHQ9PAx8JCINiqxwm1mZmXVKPW4DnAx8QCjgX0y97RvA01FlSEvFm7DuPRhyuQZ9kOwxg4NvgS3Lw4Q2he7oJ+CQ2+JOIZIIUR5x7weMM7PpwLvAy+7+HHA18EMzm0foEvZAhBkaN/dOaNkJ+p0XawwpQN2OgR6fh/d/DdvWxJ0mXu36Q+eRcacQSYQoW5VPBw7ew/IFhOvd8du8FJY+CQf8EFq0jTuNFKKD/gteHBm+hwMviDtNPBb/L1RthgHfijuJSCIU+CQjNeFIe/AlcQeRQtVpOJz+AXQYHHeS+My5I9yrcIukpbCHPG3bBw5/KNyLxKW2aG/NQrfHXFNVCWsnhcsGIpKWwi3cq8eFsclFcsGyZ+DvPWHd1LiTZNead8LMaZp/WyRthVm43WHSZfDOBeGxSNy6HRPGyZ96TdxJsqt8PGBQ9pm4k4gkRmEW7tWvwsezYMgV6gImuaGkEwy7Dlb+E1a9Gnea7KlcCp0PCj+/iKSlsAr3wrHw977w2skU2o8uCTD4YijtDVOvBq+JO012HHY/nPJ23ClEEqVwqtfCsTDxQqhcnFpQA+9eHJaL5ILi1jDyl7BuWmFd6y5uFXcCkUQpnMI97Tqo3m1oyerKsFwkV/QdA2fMhX0KYMzuuX+A10+H6u1xJxFJlMIp3JVLmrZcJA5WBO36hcdby+PNErUVL8LGeRqfXKSJCqdwl/Zu2nKROE2/AZ4fDjs2xJ0kGl4DFW+o/7bIXiicwj3y5jBtYF3FpWG5SK7Z/wzYVgHv/ybuJNFYPxN2rFfhFtkLhVO4+42B0fdBaR/Awv3o+8JykVzTZRT0/k+YfRtsWRl3mswrHx/uy46ON4dIAhVO4YZQpM9eBF+tCfcq2pLLRt4MNdthxo1xJ8mshWNhZupneuVY9ewQaaLCKtwiSdJ+AAy6KMwctmNj3Gkyo7Zb5raPwvPKJeG5irdI2lS4RXLZiJvgjDnQsn3cSZpvy0qYfLm6ZYo0U4FP6ymS40o6h/uaati+Blp3izdPU+3YBHPvgmVPhwlF6qNumSJp0xG3SBL86/Mw4dzcnxSnpio0PFv+fHheVALv3xq6f434JbTeb8+fU7dMkbTpiFskCXqeDe9eBMufhZ5nxp1mVzs2waqXwlH1iudh2xroNAL2Pz0MrnLWIijpGN7btm+4pl33dLm6ZYo0iY64RZJgwLeg/eAw7WdNVdxpYGvFzscTLwhnA5Y/C/udBkc9Bie/sfP12qIN6pYpkgHmuX7qDRg1apRPmjQp7hgi8Vr6ZCiQh90PA76d3W27w8fvw/JnUterJ8IZH4aW72smQdUmKDsKinQSTyQTzGyyu4/a02v6LRNJip5fgK5HwKKx2S3cayfDG/8Jm+aH5/scGlq7t2gbnnfZ4/8tIhIRFW6RpDALp6GjbFle93p1t2PCHwht+4bT9EOvCkOxlu4f3fZFpFEq3CJJUls0d2wCaqBlh8ysd/4D4VT8qlehZlvohtZhaHitVRc4/oXMbEdEmk2FWyRpdmyC54aEscw/fVvTP197vXrd1J2Nwhb8GSqXwaDvhVbrZUdBUcvM5haRjFDhFkmalu1gv8/Ch3fDkMuhXd/GP1NTBR+9FU6BL3s6XK8uagk9zwrrO/YZaNkxnI4XkZym7mAiSTTiRrAimP6z+t+zYxNUbw2P59wZJvSY+/twvfrQe+DMhaFoA5R0UtEWSQgdcYskUWlP6H4SLHo4tDIv7R0GMdn3hNCfetnT4Xr1EX+BPl+C3l+Etr1hv1PzY9xzkQKmwi2SRAvHwupXU08cKhfD218HasKidv1h0MXQ8cDwvG3vcBORxFPhFkmiaddB9ZbdFtaE69QnvwEdh+nUt0ieUuEWSaL6ZtPasQE6Dc9uFhHJKjVOE0mi+mbT0ixbInlPhVskiUbeHGbVqkuzbIkUBBVukSTSLFsiBUvXuEWSqt8YFWqRAqQjbhERkQRR4RYREUkQFW4REZEEUeEWERFJEBVuERGRBFHhFhERSRAVbhERkQRR4RYREUkQFW4REZEEUeEWERFJEBVuERGRBFHhFhERSRAVbhERkQRR4RYREUkQc/e4MzTKzCqAxRlcZVfgowyurxBpHzaf9mFmaD82n/Zh82V6H/Zx97I9vZCIwp1pZjbJ3UfFnSPJtA+bT/swM7Qfm0/7sPmyuQ91qlxERCRBVLhFREQSpFAL931xB8gD2ofNp32YGdqPzad92HxZ24cFeY1bREQkqQr1iFtERCSRVLhFREQSJO8Kt5l91szmmNk8M7tmD69fZGYzzGyqmb1hZgfWee0nqc/NMbNTs5s8d+ztPjSzk81scuq1yWZ2QvbT54bmfA9Tr/c2s01mdlX2UueWZv4ujzCzt81sVuo9rbObPjc043e5pZn9OfXaB2b2k+ynzx2N7cc67zvXzNzMRtVZlvm64u55cwOKgflAf6AEmAYcuNt7OtR5fCbwj9TjA1PvbwX0S62nOO6fKWH78GCgR+rxcGB53D9P0vZhnWWPA48BV8X98yRtHwItgOnAyNTzLvpdbvI+/CrwSOpxKbAI6Bv3z5Sr+zH1vvbAeODfwKjUskjqSr4dcY8G5rn7AnffDjwCnFX3De6+oc7TtkBt67yzCF/Ube6+EJiXWl+h2et96O5T3H1FavksoI2ZtcpC5lzTnO8hZnY2sJCwDwtVc/bhKcB0d5+Wet8ad6/OQuZc05x96EBbM2sBtAG2A3XfW0ga3Y8pvwBuAbbWWRZJXcm3wr0/sLTO82WpZbsws0vMbD5wK3B5Uz5bAJqzD+s6F3jP3bdFkjK37fU+NLN2wNXAjVnImcua8z0cDLiZ/dPM3jOzH0eeNjc1Zx8+DmwGVgJLgN+6+9po4+asRvejmR0C9HL355v62b2Rb4U7Le5+t7sPIPwH+dO48yRRQ/vQzIYR/vL8bhzZkqKeffhz4HZ33xRbsASpZx+2AI4CxqTuv2BmJ8YUMefVsw9HA9VAD8Ip3ivNrH9MEXOamRUBtwFXZmub+Va4lwO96jzvmVpWn0eAs/fys/mqOfsQM+sJPAV83d3nR5Iw9zVnHx4G3Gpmi4DvA9ea2aVRhMxxzdmHy4Dx7v6Ru1cCLwCHRJIytzVnH36VcL17h7uXA28ChTqWeWP7sT2hTc/rqd/bw4FnUg3UoqkrcV/4z3AjghbAAsJfiLWNCIbt9p5BdR6fAUxKPR7Gro0IFlCYDVqasw87pd5/Ttw/R1L34W7v+TmF2zitOd/DzsB7hEZVLYBXgNPj/pkStg+vBh5KPW4LvA+MiPtnytX9uNv7X2dn47RI6kqLeup5Irl7Vero5J+EloAPuvssM7uJ8IV8BrjUzE4CdgDrgG+kPjvLzB4lfEGrgEu8ABu0NGcfApcCA4Hrzez61LJTPPzFXjCauQ+FZv8urzOz24B3CY2sXvBPXnvMe838Ht4NPGRmswAjFPHp2f8p4pfmfqzvs5HUFQ15KiIikiD5do1bREQkr6lwi4iIJIgKt4iISIKocIuIiCSICreIRCY1WcWTZvammfWNO49IPlDhFpEojSSMu/4b4JyYs4jkBRVukYQys+rUdIwzzexZM+vUxM+/Xjv9oJm90NTP17POg83sgTqLZgKfI8x09nSd930+1Q9WRJpIhVskuba4+0HuPhxYC1yytyty98+5+/oMZLoWuLPO8x2EUaOmEUbgqvU8cIaZlWZgmyIFRYVbJD+8TWrWITMbbWZvm9kUM3vLzIaklrcxs0fM7AMze4owXSOp1xaZWVcz62tmM+ssv8rMfp56fLmZvW9m083skd0DmFl7wrCY0+osPhmYCDwAfK12oYeRn14HPp+xPSBSIPJqyFORQmRmxcCJhOIIMBs4OjVU40nArwjTrH4PqHT3oWY2gjCed1NcA/Rz9231nFYfRTg1XtfXgP8hTFLxrpld7TuHa5wEHA082sQcIgVNR9wiydXGzKYCq4DuwMup5R2Bx1JHzrcTJjoAOAZ4GCA17nRTx56eDow1s68Rxl3e3X5ARe0TM2sLHAu86O4fEf6gOLbO+8sJ00aKSBOocIsk1xZ3PwjoQ5gIovYa9y+Acalr32cArZuwzip2/X+h7mdPJ0w+cQjh6Hn3M3Zbdnv/2YQZ4+ampjscRZgju+66tzQhm4igwi2SeB7mnL4cuDJVTDuyc87f8+u8dTxhnmXMbDgwYg+rWw10M7MuZtaK1DVoMysCern7OMKUjx2Bdrt99gPC7HC1vkaY4rWvu/cF+gKnp9YLMJhPnloXkUaocIvkAXefQjiV/RXgVuC/zGwKu7ZjuQdoZ2YfADcBk/ewnh2p1yYSTr3PTr1UDDxsZjOAKcCdu7dCd/fZQEcza29m3QhH2K/XeX0zYarN01OLjie0LheRJtC0niKSMWb2A2Cju9/fyPu6A39z9xOzk0wkf+iIW0Qy6R5gWxrv6w1cGXEWkbykI24REZEE0RG3iIhIgqhwi4iIJIgKt4iISIKocIuIiCSICreIiEiCqHCLiIgkyP8HPsM3Qt+YhoUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "c-SLOG-M1p1Q",
        "outputId": "0f24bfb8-b28a-4007-dd84-e21570c554d9"
      },
      "source": [
        "# Plot average total number of shots per run\n",
        "\n",
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize = [8,8])\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        totalShots[start:stop], \n",
        "        '--o',\n",
        "        color = 'b',\n",
        "        label = \"ADAPT-VQE\")\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        uccsdtotalShots[start:stop], \n",
        "        '--o',\n",
        "        color = 'orange',\n",
        "        label = \"UCCSD-VQE\")\n",
        "\n",
        "ax1.legend()\n",
        "\n",
        "ax1.set_ylabel(\"Average Number of Shots (total per run)\")\n",
        "ax1.set_xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"UCCSD-VQE vs ADAPT-VQE: Number of Shots ({name})\"\n",
        "\n",
        "ax1.set_title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'UCCSD-VQE vs ADAPT-VQE: Number of Shots (H2)')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "xRd68gW21p1Q",
        "outputId": "ccd0120c-44a0-4ddc-8291-85e5a750d7be"
      },
      "source": [
        "# Plot average total number of function evaluations per run\n",
        "\n",
        "# Change this to plot only part of the data\n",
        "start = 0\n",
        "stop = len(rList)\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize = [8,8])\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        nfevList[start:stop], \n",
        "        '--o',\n",
        "        color = 'b',\n",
        "        label = \"ADAPT-VQE\")\n",
        "\n",
        "ax1.plot(rList[start:stop], \n",
        "        uccsdnfevList[start:stop], \n",
        "        '--o',\n",
        "        color = 'orange',\n",
        "        label = \"UCCSD-VQE\")\n",
        "\n",
        "ax1.legend()\n",
        "\n",
        "ax1.set_ylabel(\"Average Number of Energy Evaluations (total per run)\")\n",
        "ax1.set_xlabel(\"Interatomic Distance (Å)\")\n",
        "\n",
        "title = f\"UCCSD-VQE vs ADAPT-VQE: Energy Evaluations ({name})\"\n",
        "\n",
        "ax1.set_title(title)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'UCCSD-VQE vs ADAPT-VQE: Energy Evaluations (H2)')"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUMpXdBYNxEc"
      },
      "source": [
        "### Sampling "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3668PPXnNzgi"
      },
      "source": [
        "%%capture\n",
        "\n",
        "# Number of runs over which to take the median and IQR for each point\n",
        "runs = 20\n",
        "\n",
        "# Circuit repetitions\n",
        "testShots = [2**i for i in range(5,20,2)] \n",
        "testShots = [2**i for i in range(5,14,1)] \n",
        "\n",
        "medianEnergies = []\n",
        "firstQuartiles = []\n",
        "thirdQuartiles = []\n",
        "\n",
        "for shots in testShots:\n",
        "  shots = int(shots)\n",
        "\n",
        "  energies = []\n",
        "  \n",
        "  qinstance = QuantumInstance(backend, \n",
        "                              shots = shots)\n",
        "\n",
        "  finalEnergies = []\n",
        "\n",
        "  for _ in range(runs):\n",
        "\n",
        "    myAdapt = AdaptVQE(qubitPool,\n",
        "                      molecule,\n",
        "                      qinstance = qinstance,\n",
        "                      verbose = False,\n",
        "                      threshold = threshold,\n",
        "                      maxIterations = maxIterations,\n",
        "                      shots = shots,\n",
        "                      referenceDeterminant = None\n",
        "                      )\n",
        "    \n",
        "    myAdapt.prepare()\n",
        "    myAdapt.run()\n",
        "    data = myAdapt.data\n",
        "\n",
        "    finalEnergy = data.result[\"energy\"]\n",
        "    finalEnergies.append(finalEnergy)\n",
        "\n",
        "  #runAverage = np.mean(finalEnergies)\n",
        "  #runSTD = np.std(finalEnergies)\n",
        "  median = np.median(finalEnergies)\n",
        "  firstQuartile = median - np.percentile(finalEnergies,25)\n",
        "  thirdQuartile = np.percentile(finalEnergies,75) - median\n",
        "\n",
        "  medianEnergies.append(median)\n",
        "  firstQuartiles.append(firstQuartile)\n",
        "  thirdQuartiles.append(thirdQuartile)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "i3YmiWwZdzoU",
        "outputId": "a5ab4029-ac36-4a6d-f7d6-1c87e0ba4e79"
      },
      "source": [
        "fig, ax1 = plt.subplots(figsize=[8,8])\n",
        "\n",
        "title = \"ADAPT-VQE: Effect of Sampling Noise ({})\".format(name)\n",
        "\n",
        "ax1.set_title(title)\n",
        "\n",
        "start = 0\n",
        "stop = len(testShots)\n",
        "\n",
        "errors = [energy - exactEnergy for energy in medianEnergies]\n",
        "\n",
        "ax1.errorbar(testShots[start:stop], \n",
        "             #errors[start:stop], \n",
        "             #medianEnergies[start:stop],\n",
        "             #medianEnergies[start:stop],\n",
        "             [- mE for mE in medianEnergies[start:stop]],\n",
        "             fmt = '--o',\n",
        "             # REVERSED beacause of the negative\n",
        "             yerr = (thirdQuartiles[start:stop],firstQuartiles[start:stop]),\n",
        "             capsize = 3)\n",
        "\n",
        "ax1.set_xlabel(\"Number of Shots\")\n",
        "ax1.set_ylabel(\"- Energy (au)\")\n",
        "ax1.set_yscale(\"log\")\n",
        "ax1.set_xscale(\"log\")\n",
        "\n",
        "caMin = exactEnergy - chemicalAccuracy\n",
        "caMax = exactEnergy + chemicalAccuracy\n",
        "#l = ax1.axhspan(caMin,caMax, alpha=0.3, color = 'cornflowerblue')\n",
        "l = ax1.axhspan(-caMax, -caMin, alpha=0.3, color = 'cornflowerblue')\n",
        "\n",
        "plt.axhline(y = -exactEnergy, color = 'cornflowerblue' , linestyle = '-')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7fe58f75ad10>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f661dWFBRtwR"
      },
      "source": [
        "### Decoherence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7Yi0rt4Me7r"
      },
      "source": [
        "def getDecNoiseModel(t1,t2,measTime,resetTime,cxTime,uTime,qubitNumber,basis_gates):\n",
        "  '''\n",
        "  Creates a noise model with decoherence only.\n",
        "  Must have t2 <= 2t1\n",
        "\n",
        "  Arguments:\n",
        "    t1 (float): relaxation time\n",
        "    t2 (float): dephasing time\n",
        "    measTime (float): measurement time\n",
        "    resetTime (float): reset time\n",
        "    cxTime: \n",
        "    qubitNumber (int): the number of qubits of the backend. The error\n",
        "      will be assumed to apply to all (connected) pairs of qubits.\n",
        "    basis_gates (list): the basis gates to be assumed in the noise model\n",
        "  \n",
        "  Returns:\n",
        "    noiseModel (qiskit.providers.aer.noise.noise_model.NoiseModel):\n",
        "      noise model for decoherence\n",
        "  '''\n",
        "\n",
        "  noiseModel = NoiseModel(basis_gates=belemNM.basis_gates)\n",
        "\n",
        "  # Thermal relaxation\n",
        "  uError = thermal_relaxation_error(t1,t2,uTime)\n",
        "  measError = thermal_relaxation_error(t1, t2, measTime)\n",
        "  resetError = thermal_relaxation_error(t1, t2, resetTime)\n",
        "  cxTError = thermal_relaxation_error(t1, t2, cxTime)\n",
        "  cxTError = cxTError.tensor(cxTError)\n",
        "\n",
        "  for i in range(qubitNumber):\n",
        "\n",
        "    # Add thermal relaxation on each qubit measurement\n",
        "    noiseModel.add_quantum_error(measError,\"measure\",[i])\n",
        "    noiseModel.add_quantum_error(resetError,\"reset\",[i])\n",
        "\n",
        "    # Add thermal relaxation on single qubit gates\n",
        "    noiseModel.add_quantum_error(uError,\"id\",[i])\n",
        "    noiseModel.add_quantum_error(uError,\"rz\",[i])\n",
        "    noiseModel.add_quantum_error(uError,\"x\",[i])\n",
        "    noiseModel.add_quantum_error(uError,\"sx\",[i])\n",
        "\n",
        "    # Add thermal relaxation on each possible pair of CNOTs\n",
        "    for j in range(qubitNumber):\n",
        "      if j != i:\n",
        "        noiseModel.add_quantum_error(cxTError,\"cx\",[i,j])\n",
        "\n",
        "    return noiseModel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsILyfTURyMd"
      },
      "source": [
        "%%capture\n",
        "\n",
        "# Number of runs over which to take the average and std for each point\n",
        "runs = 20\n",
        "\n",
        "shots = 2**17\n",
        "\n",
        "# Using IBMQ_Belem data\n",
        "qubitNumber = 5\n",
        "\n",
        "# Basic times\n",
        "# All nanoseconds\n",
        "t1 = 100E3\n",
        "t2 = 2*t1\n",
        "\n",
        "# Times for operations\n",
        "measTime = 5000 # Measuring\n",
        "resetTime = 5000 # Reset\n",
        "cxTime = 500 # CNOT gate\n",
        "uTime = 100 # Single qubit gate\n",
        "\n",
        "# Multipliers for t1, t2. 1 = 100%; 2 = 200%; ...\n",
        "# Will correspond to graph points. 100% of t1, t2; \n",
        "#then 200%;...\n",
        "multipliers = [i/40 for i in range(1,10)]\n",
        "multipliers = [i/40 for i in range(2,8000,1000)]\n",
        "\n",
        "medianEnergies = []\n",
        "firstQuartiles = []\n",
        "thirdQuartiles = []\n",
        "\n",
        "for mult in multipliers:\n",
        "  energies = []\n",
        "  t1mult = mult * t1\n",
        "  t2mult = mult * t2\n",
        "\n",
        "  myNoiseModel = getDecNoiseModel(t1mult,\n",
        "                                  t2mult,\n",
        "                                  measTime,\n",
        "                                  resetTime,\n",
        "                                  cxTime,\n",
        "                                  uTime,\n",
        "                                  qubitNumber,\n",
        "                                  basis_gates)\n",
        "  \n",
        "  qinstance = QuantumInstance(backend, \n",
        "                              shots = shots,\n",
        "                              coupling_map=coupling_map,\n",
        "                              basis_gates=basis_gates,\n",
        "                              noise_model=myNoiseModel)\n",
        "\n",
        "  finalEnergies = []\n",
        "  \n",
        "  for _ in range(runs):\n",
        "\n",
        "    myAdapt = AdaptVQE(qubitPool,\n",
        "                      molecule,\n",
        "                      qinstance = qinstance,\n",
        "                      verbose = False,\n",
        "                      threshold = threshold,\n",
        "                      maxIterations = maxIterations,\n",
        "                      shots = shots,\n",
        "                      referenceDeterminant = None\n",
        "                      )\n",
        "    myAdapt.prepare()\n",
        "    myAdapt.run()\n",
        "    data = myAdapt.data\n",
        "\n",
        "    finalEnergy = data.result[\"energy\"]\n",
        "    finalEnergies.append(finalEnergy)\n",
        "    \n",
        "  median = np.median(finalEnergies)\n",
        "  firstQuartile = median - np.percentile(finalEnergies,25)\n",
        "  thirdQuartile = np.percentile(finalEnergies,75) - median\n",
        "\n",
        "  medianEnergies.append(median)\n",
        "  firstQuartiles.append(firstQuartile)\n",
        "  thirdQuartiles.append(thirdQuartile)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "oGu5H0ZpRqUK",
        "outputId": "506ee92c-a382-4cca-86b5-38f1e971c751"
      },
      "source": [
        "fig, ax1 = plt.subplots(figsize=[8,8])\n",
        "\n",
        "title = \"ADAPT-VQE: Effect of Decoherence\\n{}, \".format(name)\n",
        "title += \"{} shots\".format(shots)\n",
        "\n",
        "ax1.set_title(title)\n",
        "\n",
        "start = 0\n",
        "stop = len(multipliers)\n",
        "times = [multiplier * t1 * 10**(-9) for multiplier in multipliers]\n",
        "\n",
        "errors = [energy - exactEnergy for energy in medianEnergies]\n",
        "ax1.errorbar(times[start:stop], \n",
        "             errors[start:stop], \n",
        "             fmt = '--o',\n",
        "             yerr = (firstQuartiles[start:stop],thirdQuartiles[start:stop]),\n",
        "             capsize = 3)\n",
        "\n",
        "\n",
        "ax1.set_xlabel(\"T1 = T2/2 (s)\")\n",
        "ax1.set_ylabel(\"Error (au)\")\n",
        "ax1.set_yscale(\"log\")\n",
        "\n",
        "minAccuracy = exactEnergy - chemicalAccuracy\n",
        "maxAccuracy = exactEnergy + chemicalAccuracy\n",
        "l = ax1.axhspan(0, chemicalAccuracy, alpha=0.3, color = 'cornflowerblue')\n",
        "\n",
        "plt.axhline(y = exactEnergy, color = 'cornflowerblue' , linestyle = '-')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f9adaf9d210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpTNLqviVRlf"
      },
      "source": [
        "### Readout Errors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VV37soauUYGD"
      },
      "source": [
        "%%capture\n",
        "\n",
        "# Number of runs over which to take the median and IQR for each point\n",
        "runs = 20\n",
        "\n",
        "# Circuit repetitions\n",
        "shots = 2**17\n",
        "\n",
        "# Measure 1 prep 0 average\n",
        "m1p0 = 0.01 \n",
        "# Measure 0 prep 1 average\n",
        "m0p1 = 5 * m1p0\n",
        "\n",
        "multipliers = [i/10 for i in range(0,21,4)]\n",
        "#multipliers = [i/10 for i in range(0,40,4)] # ADAPT only\n",
        "\n",
        "medianEnergies = []\n",
        "firstQuartiles = []\n",
        "thirdQuartiles = []\n",
        "\n",
        "for mult in multipliers:\n",
        "\n",
        "  energies = []\n",
        "\n",
        "  # Readout Error\n",
        "  readoutErrorMatrix = [[1 - m1p0 * mult, m1p0 * mult],\n",
        "                        [m0p1 * mult, 1 - m0p1 * mult]]\n",
        "  readoutError = ReadoutError(readoutErrorMatrix)\n",
        "\n",
        "  noiseModel = NoiseModel()\n",
        "  for i in range(qubitNumber):\n",
        "    # Adde readout error on each qubit\n",
        "    noiseModel.add_readout_error(readoutError,[i])\n",
        "  \n",
        "  qinstance = QuantumInstance(backend, \n",
        "                              shots = shots,\n",
        "                              coupling_map=coupling_map,\n",
        "                              basis_gates=basis_gates,\n",
        "                              noise_model=noiseModel)\n",
        "\n",
        "  finalEnergies = []\n",
        "\n",
        "  for _ in range(runs):\n",
        "\n",
        "    myAdapt = AdaptVQE(qubitPool,\n",
        "                      molecule,\n",
        "                      qinstance = qinstance,\n",
        "                      verbose = False,\n",
        "                      threshold = threshold,\n",
        "                      maxIterations = maxIterations,\n",
        "                      shots = shots,\n",
        "                      referenceDeterminant = None\n",
        "                      )\n",
        "    myAdapt.prepare()\n",
        "    myAdapt.run()\n",
        "    data = myAdapt.data\n",
        "\n",
        "    finalEnergy = data.result[\"energy\"]\n",
        "    finalEnergies.append(finalEnergy)\n",
        "\n",
        "  #runAverage = np.mean(finalEnergies)\n",
        "  #runSTD = np.std(finalEnergies)\n",
        "  median = np.median(finalEnergies)\n",
        "  firstQuartile = median - np.percentile(finalEnergies,25)\n",
        "  thirdQuartile = np.percentile(finalEnergies,75) - median\n",
        "\n",
        "  medianEnergies.append(median)\n",
        "  firstQuartiles.append(firstQuartile)\n",
        "  thirdQuartiles.append(thirdQuartile)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "2CDin25obDPE",
        "outputId": "1ddb495e-14c2-4971-d424-00a584d38a9c"
      },
      "source": [
        "fig, ax1 = plt.subplots(figsize=[8,8])\n",
        "\n",
        "title = \"ADAPT-VQE: Effect of Readout Errors\\n{}, \".format(name)\n",
        "title += \"{} shots\".format(shots)\n",
        "\n",
        "ax1.set_title(title)\n",
        "\n",
        "start = 0\n",
        "stop = len(multipliers)\n",
        "m0p1s = [multiplier * m0p1 for multiplier in multipliers]\n",
        "\n",
        "errors = [energy - exactEnergy for energy in medianEnergies]\n",
        "ax1.errorbar(m0p1s[start:stop], \n",
        "             errors[start:stop], \n",
        "             fmt = '--o',\n",
        "             yerr = (firstQuartiles[start:stop],thirdQuartiles[start:stop]),\n",
        "             capsize = 3)\n",
        "\n",
        "\n",
        "ax1.set_xlabel(\"m0p1 = 5 * m1p0\")\n",
        "ax1.set_ylabel(\"Error (au)\")\n",
        "ax1.set_yscale(\"log\")\n",
        "\n",
        "l = ax1.axhspan(0, chemicalAccuracy, alpha=0.3, color = 'cornflowerblue')\n",
        "\n",
        "plt.axhline(y = exactEnergy, color = 'cornflowerblue' , linestyle = '-')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7f4a60fd8e90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FsiUtJFuuPKD"
      },
      "source": [
        "### Noise Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyhX5BV9uWRj"
      },
      "source": [
        "%%capture\n",
        "\n",
        "# Number of runs over which to take the average and std for each point\n",
        "runs = 20\n",
        "\n",
        "shots = 2**17\n",
        "\n",
        "# Using IBMQ_Belem data\n",
        "qubitNumber = 5\n",
        "\n",
        "# Basic times; t1 is from ibmq_belem\n",
        "# All nanoseconds\n",
        "t1 = 86.91E3\n",
        "t2 = 2*t1\n",
        "\n",
        "# Measure / CNOT gate times. FrommBelem\n",
        "measTime = 5351.111\n",
        "cxTime = 552.889\n",
        "\n",
        "# Measure 0 prep 1 average\n",
        "m0p1 = 0.0406\n",
        "# Measure 1 prep 0 average\n",
        "m1p0 = 0.0096 \n",
        "\n",
        "\n",
        "# Multipliers for t1, t2. 1 = 100%; 2 = 200%; ...\n",
        "# Will correspond to graph points. 100% of t1, t2; \n",
        "#then 200%;...\n",
        "multipliers = [i/5 for i in range(1,12)]\n",
        "multipliers = [i/5 for i in range(1,2250,200)]\n",
        "\n",
        "medianEnergies = []\n",
        "firstQuartiles = []\n",
        "thirdQuartiles = []\n",
        "\n",
        "for mult in multipliers:\n",
        "  energies = []\n",
        "  t1mult = mult * t1\n",
        "  t2mult = mult * t2\n",
        "\n",
        "  myNoiseModel = createNoiseModel(t1mult,t2mult,measTime,cxTime,m0p1,m1p0)\n",
        "  qinstance = QuantumInstance(backend, \n",
        "                              shots = shots,\n",
        "                              coupling_map=coupling_map,\n",
        "                              basis_gates=basis_gates,\n",
        "                              noise_model=myNoiseModel)\n",
        "\n",
        "  finalEnergies = []\n",
        "  \n",
        "  for _ in range(runs):\n",
        "\n",
        "    myAdapt = AdaptVQE(qubitPool,\n",
        "                      molecule,\n",
        "                      qinstance = qinstance,\n",
        "                      verbose = False,\n",
        "                      threshold = threshold,\n",
        "                      maxIterations = maxIterations,\n",
        "                      shots = shots,\n",
        "                      referenceDeterminant = None\n",
        "                      )\n",
        "    myAdapt.prepare()\n",
        "    myAdapt.run()\n",
        "    data = myAdapt.data\n",
        "\n",
        "    finalEnergy = data.result[\"energy\"]\n",
        "    finalEnergies.append(finalEnergy)\n",
        "    \n",
        "  median = np.median(finalEnergies)\n",
        "  firstQuartile = median - np.percentile(finalEnergies,25)\n",
        "  thirdQuartile = np.percentile(finalEnergies,75) - median\n",
        "\n",
        "  medianEnergies.append(median)\n",
        "  firstQuartiles.append(firstQuartile)\n",
        "  thirdQuartiles.append(thirdQuartile)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "q75h-zKjIcG_",
        "outputId": "f215431a-0d53-4446-fc05-1e6d90d8ec55"
      },
      "source": [
        "fig, ax1 = plt.subplots(figsize=[8,8])\n",
        "\n",
        "title = \"ADAPT-VQE: Effect of Decoherence\\n{}, \".format(name)\n",
        "title += \"{} shots\".format(shots)\n",
        "\n",
        "ax1.set_title(title)\n",
        "\n",
        "start = 0\n",
        "stop = len(multipliers)\n",
        "times = [multiplier * t1 * 10**(-9) for multiplier in multipliers]\n",
        "ax1.errorbar(times[start:stop], \n",
        "             medianEnergies[start:stop], \n",
        "             fmt = '--o',\n",
        "             yerr = (firstQuartiles[start:stop],thirdQuartiles[start:stop]),\n",
        "             capsize = 3)\n",
        "\n",
        "\n",
        "ax1.set_xlabel(\"T1 = T2/2 (s)\")\n",
        "ax1.set_ylabel(\"Energy (au)\")\n",
        "\n",
        "minAccuracy = exactEnergy - chemicalAccuracy\n",
        "maxAccuracy = exactEnergy + chemicalAccuracy\n",
        "l = ax1.axhspan(minAccuracy, maxAccuracy, alpha=0.3, color = 'cornflowerblue')\n",
        "\n",
        "plt.axhline(y = exactEnergy, color = 'cornflowerblue' , linestyle = '-')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "QiViCl6Nlb8X",
        "outputId": "27ee851c-b3b6-4e0a-ba10-08c28c3192ae"
      },
      "source": [
        "#errors\n",
        "#(medianEnergies, firstQuartiles, thirdQuartiles) = data\n",
        "fig, ax1 = plt.subplots(figsize=[8,8])\n",
        "\n",
        "title = \"ADAPT-VQE: Effect of Decoherence\\n{}, \".format(name)\n",
        "title += \"{} shots\".format(shots)\n",
        "\n",
        "ax1.set_title(title)\n",
        "\n",
        "start = 1\n",
        "stop = len(multipliers)\n",
        "times = [multiplier * t1 * 10**(-9) for multiplier in multipliers]\n",
        "\n",
        "errors = [np.abs(exactEnergy - energy) for energy in medianEnergies]\n",
        "\n",
        "ax1.errorbar(times[start:stop], \n",
        "             #medianEnergies[start:stop], \n",
        "             errors[start:stop],\n",
        "             fmt = '--o',\n",
        "             yerr = (firstQuartiles[start:stop],thirdQuartiles[start:stop]),\n",
        "             capsize = 3)\n",
        "\n",
        "\n",
        "ax1.set_xlabel(\"T1 = T2/2 (s)\")\n",
        "ax1.set_ylabel(\"Error (au)\")\n",
        "\n",
        "ax1.set_yscale('log')\n",
        "\n",
        "minAccuracy = exactEnergy - chemicalAccuracy\n",
        "maxAccuracy = exactEnergy + chemicalAccuracy\n",
        "l = ax1.axhspan(0, chemicalAccuracy, alpha=0.3, color = 'cornflowerblue')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usENoaf29Qu5"
      },
      "source": [
        "## Downloading / Uploading Data to/from File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_1mfGg7Ge9D"
      },
      "source": [
        "fileName = molecule.description + \"_\" + backend.name() + \"_\" + str(data.shots) + \"_\" + str(maxIterations) + \".pkl\"\n",
        "print(fileName)\n",
        "\n",
        "myFile = open(fileName, \"wb\")\n",
        "pickle.dump(data, myFile)\n",
        "myFile.close()\n",
        "\n",
        "files.download(fileName) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "3DBbzgYDgNKO",
        "outputId": "c66c2ecf-6df0-4892-b1d3-66eca7882bdb"
      },
      "source": [
        "fileName = \"adapt_readout.pkl\"\n",
        "data = (medianEnergies, firstQuartiles, thirdQuartiles)\n",
        "\n",
        "myFile = open(fileName, \"wb\")\n",
        "pickle.dump(data, myFile)\n",
        "myFile.close()\n",
        "\n",
        "files.download(fileName) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_e1fbf3b3-5114-468a-89b5-4ff024e04360\", \"adapt_readout.pkl\", 507)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "V_xMAdCuyLsd",
        "outputId": "b1bb9de8-b4f9-4713-ee65-9dbadc965b1a"
      },
      "source": [
        "# Upload from computer\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))\n",
        "  \n",
        "myFile = open(fn, \"rb\")\n",
        "data = pickle.load(myFile)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-db8205ce-eabb-4361-aabc-9add8e2b0529\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-db8205ce-eabb-4361-aabc-9add8e2b0529\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving adapt_readout.pkl to adapt_readout (1).pkl\n",
            "User uploaded file \"adapt_readout.pkl\" with length 507 bytes\n"
          ]
        }
      ]
    }
  ]
}