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.
A repository for finishing my undergraduate thesis titled: Quantum Image Classifier Design with Data Re-uploading Quantum Convolution and Data Re-uploading Classifier Scheme.
variational quantum circuit simulator in Julia, under GPLv3
A quantum reinforcement learning framework based on PyTorch and PennyLane.
This repository contains the source code of Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry
variational quantum circuit
Variational Quantum Algorithms for Unsupervised Image Segmentation
QuACS: Variational Quantum Algorithm for Coalition Structure Generation in Induced Subgraph Games
Supplemental code for "Variational Quantum Optimization of Nonlocality in Noisy Quantum Networks"
Quantum Classical Recurrent for Music Generation
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.
A hybrid Quantum-Classical Transformer implementation based on nanoGPT, using PyTorch and PennyLane to replace attention heads with Variational Quantum Circuits (VQC).
Quantum Computing applied Monte Carlo Simulations
Hybrid Quantum-Classical Neural Network integrating parameterised quantum circuits as trainable PyTorch layers. Benchmarked against classical baseline on real datasets using PennyLane + PyTorch.
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.
Notebooks for the Seminar in ETSIT (UPM) - Master of Science in Signal Theory and Communication (Track: SIGNAL PROCESSING AND MACHINE LEARNING FOR BIG DATA).
Hybrid classical-quantum neural network that mitigates real IBM Quantum hardware errors. Achieved +4.57% fidelity improvement on ibm_fez (5 qubits) using Qiskit + PyTorch.
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