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13/10 added qSLP and Utils_general:

  • qSLP.ipynb: is a step by step creation of a working qSLP.
  • Utils_general: contains a function that accepts multiple parameters and allow the caller to change the state preparation of the circuit. It implements circuits from both the files Generalised qSLP and Experiment.ipynb

From the repeated runs of the function we observe the following results for the MNIST dataset:

Pad Siqbit d AcTrain AcTest Cost Iteration
T F 1 0.919 0.844 0.072 3
T F 2 0.854 0.777 0.066 3
T F 3 0.496 0.533 0.044 3
T F 4 0.726 0.689 0.042 3
T F 5 0.704 0.767 0.051 2
F T 1 0.481 0.667 0.000 1
F T 2 0.481 0.667 0.002 1
F T 3 0.481 0.667 0.002 1
F T 4 0.481 0.667 0.003 1
F T 5 0.481 0.667 0.005 1

Here we can clearly see that some results are wrong, namely the one with Pad=False. Solving this error will be the main objective going forward.

A qbit count is not present but to obtain how many qbit are used by the circuit we just solve: $$ d + 4^{p} $$ where $$p = 0: \it{if}:: \bold{Siqbit}== True,$$$$ p = 1 ::\it{otherwise} $$

7/11 modified qSLP

Now the Pad=False outputs a different, apparently working, result.

9/11 added QVSM

  • QVSM: step by step creation of a quantum SVM both with qiskit QSVC and with sikitlearn SVC

runned QVSC on the MINS dataset and obtained: kernel classification test score: 0.9333333333333333 Note that this result don't change when changing the feature_dim parameter, suggesting a possible problem in the initialization.

16/11 added qiskit QNN

Added:

  • qnnc.ipybn a step by step construction and training of the two classification methods for NN made by qiskit.
  • IBMQ.csv containing results from qncc.ipynb
  • qSLP.csv containing results from qSLP.ipynb

Changed the overall structure of the repository for a better understanding of the content.

From the experiments runned it appears that both QVSM and QNNC utilize the same state preparation techniques. In the experiments this corresponds in the ZZFeatureMap but this can be changed in any feature map from the library.

26/11/2021

Slightly modified collect_results.ipynb to return the mean results in markdown

(dataset, padding, control Qbits) ac_train ac_test cost_test
('MNIST', False, 1) 0.66675 0.6003 0.053
('MNIST', False, 2) 0.664333 0.5553 0.0513
('MNIST', False, 3) 0.67 0.6 0.057
('MNIST', False, 4) 0.6595 0.533 0.057
('MNIST', True, 1) 0.686667 0.5557 0.0427
('MNIST', True, 2) 0.7315 0.5503 0.066
('MNIST', True, 3) 0.6266 0.48 0.0514
('MNIST', True, 4) 0.716333 0.6443 0.0423
('MNIST', True, 5) 0.6815 0.667 0.053
('breast', False, 1) 0.526 0.4 0.017
('breast', False, 2) 0.563 0.333 0.023
('breast', False, 3) 0.57 0.4 0.02
('breast', False, 4) 0.563 0.333 0.033
('breast', True, 1) 0.519 0.333 0.054
('breast', True, 2) 0.496 0.533 0.033
('breast', True, 3) 0.496 0.533 0.032
('breast', True, 4) 0.489 0.6 0.028
('moon', False, 1) 0.8 0.6 0.038
('moon', False, 2) 0.756 0.9 0.043
('moon', False, 3) 0.778 0.5 0.041
('moon', False, 4) 0.8 0.5 0.043
('moon', True, 1) 0.833 0.9 0.149
('moon', True, 2) 0.744 0.9 0.056
('moon', True, 3) 0.533 0.6 0.046
('moon', True, 4) 0.633 0.7 0.05

27/11/2021

Changed sQLP: the circuit involving padding now uses cU3 instead of temporary qubits with swaps.

28/11/2021

Utils_general cleaning.

29/11/2021

Started experimenting with different initial parameters for training. The chosen approach is to obtain a random sample from a normal distribution with mean 1 and std 1.

30/11/2021

new results available for the MNIST dataset comparing classes 0-9 and 3-8. The best results are:

name d pad acc_train acc_test cost_test training time (s)
86 MNIST09 1 False 0.767 0.7 0.027 203.914
42 MNIST09 2 False 0.767 0.6 0.04 607.436
72 MNIST09 1 True 0.922 0.8 0.053 801.578
68 MNIST09 2 True 0.922 0.9 0.072 1163.92
20 MNIST38 1 False 0.656 0.6 0.032 229.683
105 MNIST38 2 False 0.578 0.6 0.013 585.79
87 MNIST38 1 True 0.8 0.9 0.212 836.254
59 MNIST38 2 True 0.8 0.9 0.208 1228.69
157 iris01 1 False 0.833 1 0.002 259.099
151 iris01 2 False 0.822 0.9 0.003 598.938
148 iris01 1 True 0.633 0.6 0.047 852.883
166 iris01 2 True 0.667 0.6 0.035 1215.74
182 iris02 1 False 0.878 0.9 0.007 294.972
164 iris02 2 False 0.9 0.9 0.007 599.695
149 iris02 1 True 0.989 1 0.143 783.584
155 iris02 2 True 1 1 0.012 1217.35
141 iris12 1 False 0.544 0.5 0.016 267.435
150 iris12 2 False 0.544 0.5 0.015 599.481
147 iris12 1 True 0.533 0.5 0.02 828.236
177 iris12 2 True 0.544 0.5 0.024 1217.25

Padding seems to work better but requires more than twice the training time. Launched experiments with every Iris combination.

01/12/2021

Corrected table from 30/01. Changed file collect_results.ipybn into a utility module result_handler.py that given the csv file with all the results can store the best in another file along with their parameters and (store_best_param) and can read and return the desidered parameters (get_best_par). Changed the format of qSLP_result to include parameter after the optimization. Cleaned Utils_general.py, utils_pad.py, and Utils.py from unused and duplicated methods.

06/12/2021

  • qSLP.py: new file containing a new version of qSLP inclosed in a class. It contains the class Pad_circ that stores a parametrized vesion of qSLP with padding. This class can be used to insert qSLP into CircuitQNN and NeuralNetworkClassifier to train the model like the ones in the qiskit library. The class qSLP is still missing a parametrized version of the non padded state preparation.

09/12/2021

  • temp.py playground to test qSLP.py Known problems:
  • It happears that some experiments have some difficulties in moving in the loss landscape and often get trapped for many iteration in some local minima.
  • Couldn instantiate a "paddless" parametrized state preparation

13/12/2021

  • Created a working class sQLP in sQLP.py, it takes as arguments pad and d and can create the two types of circuit. It only stores a parametried version of the state_preparation and the ansatz
  • Renamed qSLP.ipynb into old_qSLP.ipynb since now it is possible to train the model with a qiskit built-in function
  • Created qSLP.ipynb as a playgruond to test the new class. All tests were successfull and the class qSLP can be indeed used

15/12/2021

  • Created a function that returns ansatz and state preparation depending on parameters. It is now possible to merge qslp with padding with ZZFeatureMap and RealAmplitude
  • tmp.ipynb contains the first implementation of a pipeline to get and train different architectures.

1/1/2022

Terminated test. Found error in qSLP.py. I will keep the trasining data of QSVM and qNNC and test again qSLP models.

2/1/2022

Error found and corrected started another batch of tests. Partial results:

time represent the lenght of the training in seconds. Loss is the default for NeuralNetworkClassifier (squared errors)

MNIST38

dataset model d train_score test_score time final_loss
MNIST38 QSVM - 0.821875 0.8 796 -
MNIST38 qNNC_v1 - 0.546875 0.4625 195 140.8
MNIST38 qNNC_v2 - 0.775 0.75 243 122.319
MNIST38 qSLP_no_pad 1 0.690625 0.6125 178 130.128
MNIST38 qSLP_no_pad 2 0.684375 0.625 564 130.105
MNIST38 qSLP_no_pad 3 0.68125 0.625 800 130.531
MNIST38 qSLP_pad 1 0.8625 0.825 685 101.39
MNIST38 qSLP_pad 2 0.840625 0.8125 960 104.059
MNIST38 qSLP_pad 3 0.8375 0.8125 1224 112.949
MNIST09
dataset model d train_score test_score time final_loss
MNIST09 QSVM - 0.965625 0.9375 792 -
MNIST09 qNNC_v1 - 0.859375 0.8625 182 109.237
MNIST09 qNNC_v2 - 0.85 0.8375 254 95.6455
MNIST09 qSLP_no_pad 1 0.859375 0.8125 239 80.7695
MNIST09 qSLP_no_pad 2 0.865625 0.825 550 80.3418
MNIST09 qSLP_no_pad 3 0.88125 0.825 797 81.417
MNIST09 qSLP_pad 1 0.89375 0.925 617 82.8154
MNIST09 qSLP_pad 2 0.9125 0.8875 973 83.3145
MNIST09 qSLP_pad 3 0.921875 0.9375 1226 84.375

iris01

dataset model d train_score test_score time final_loss
iris01 QSVM - 1 1 47 -
iris01 qNNC_v1 - 0.925 0.85 41 19.5918
iris01 qNNC_v2 - 0.9625 0.95 55 18.501
iris01 qSLP_no_pad 1 1 1 58 11.1934
iris01 qSLP_no_pad 2 1 1 165 11.4238
iris01 qSLP_no_pad 3 1 1 213 11.6934
iris01 qSLP_pad 1 1 1 183 12.7266
iris01 qSLP_pad 2 1 1 275 13.2285
iris01 qSLP_pad 3 1 1 338 14.3496

iris12

dataset model d train_score test_score time final_loss
iris12 QSVM - 0.9625 1 48 -
iris12 qNNC_v1 - 0.925 1 50 22.2041
iris12 qNNC_v2 - 0.8875 0.8 53 26.9326
iris12 qSLP_no_pad 1 0.8 0.65 68 28.6309
iris12 qSLP_no_pad 2 0.8 0.65 159 29.1299
iris12 qSLP_no_pad 3 0.7875 0.6 212 29.1455
iris12 qSLP_pad 1 0.825 0.6 168 28.6299
iris12 qSLP_pad 2 0.825 0.65 253 28.4258
iris12 qSLP_pad 3 0.825 0.6 317 29.5859

iris02

dataset model d train_score test_score time final_loss
iris02 QSVM - 1 1 48 -
iris02 qNNC_v1 - 1 1 45 17.3779
iris02 qNNC_v2 - 1 0.95 51 17.9932
iris02 qSLP_no_pad 1 1 1 59 10.6104
iris02 qSLP_no_pad 2 1 1 164 10.5508
iris02 qSLP_no_pad 3 1 1 211 10.7529
iris02 qSLP_pad 1 1 1 186 12.3643
iris02 qSLP_pad 2 1 1 251 13.834
iris02 qSLP_pad 3 1 1 317 17.1045

6/02/2022

A lot of minor changes to complete the thesis, leaving today the develop branch and starting the more complete research branch

Creation Research branch.

07/02/2022

Cleaning of some notebooks. Results are moved on the old results folder.

8/02/2022

Added two methods to the qSLP class: Train, predict to automatically train the model.