Quantum Classical Recurrent for Music Generation

Noise-assisted Variational Quantum Thermalization (NAVQT) is an algorithm used to learn the parameters in a variational quantum circuit which prepares a thermal state of a Hamiltonian. Different from other approaches it considers the noise itself as a variational parameter which can be learned using approximations on the entropy.

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.

DDQCL implementation using Qiskit. Variational quantum circuit that maps a randomly generated set of four 4-qubit input states to four 4-qubit output states. Circuit parameters are refined over time to get the lowest cost parameter set.

Supplemental code for "Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks"

variational quantum circuit simulator in Julia, under GPLv3

A quantum reinforcement learning framework based on PyTorch and PennyLane.

variational quantum circuit

A repository for finishing my undergraduate thesis titled: Quantum Image Classifier Design with Data Re-uploading Quantum Convolution and Data Re-uploading Classifier Scheme.

This repository contains the source code of Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

A curated list of recent textbooks, reviews, perspectives, and research papers related to quantum machine learning, variational quantum algorithms, tensor networks, and classical machine learning applications in quantum systems.