This project involves simulating a quantum classifier using a variational quantum circuit for binary classification problems. It is divided into three main parts, each contributing to the total project credits.

Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification

PyTorch implementation of "Effective dimension of machine learning models" paper

Quantum version of recurrent neural networks for machine learning

This repository contains my solution to the evaluation tasks on QML-HEP for ML4Sci Umbrella Organization in GSoC 2021

Quantum architecture for predicting evolution of time series and analyze any anomalies arising in it.

FYS2029 Quantum Computing project work: quantum neural networks

Implementations of quantum circuits used for the thesis Quantum Neural Networks with Qutrits

Feed forward QNN

Code author: Collin Farquhar; paper authors: Collin Farquhar and Jithin Jagannath

A library for the rapid prototyping of hybrid quantum-classical neural networks in speech applications.

Entanglement characterization of variational quantum circuits using a Matrix Product State simulator and qiskit.

Qauntum convolutional neural network in protein distance prediction.

This repository contains the code to reproduce the results in the paper A Variational algorithm for Quantum Neural Networks, accepted in the International Conference on Computational Science 2020, Quantum Computing track.

Reimplementing the hybrid quantum-classical models from the paper "Quantum machine learning for image classification" by Arsenii Senokosov et al (https://arxiv.org/pdf/2304.09224.pdf)