quantum-machine-learning

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

A PyTorch-based framework for Quantum Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.

Introductions to key concepts in quantum machine learning, as well as tutorials and implementations from cutting-edge QML research.

Curated list of awesome papers and resources in quantum machine learning

This repository contains the source code used to produce the results presented in the paper "Continuous-variable quantum neural networks". Due to subsequent interface upgrades, these scripts will work only with Strawberry Fields version <= 0.10.0.

Quantum Machine Learning Community Course

Pythonic tool for running data-science/high performance/quantum-computing workflows in heterogenous environments.

This repository contains the source code used to produce the results presented in the paper "Machine learning method for state preparation and gate synthesis on photonic quantum computers".

QuantumFlow: A Quantum Algorithms Development Toolkit

This is the repository for https://quantumalgorithms.org

Tensor-Based Quantum Machine Learning

IEEE ICASSP 21 - Quantum Convolution Neural Networks for Speech Processing and Automatic Speech Recognition

QHack—The one-of-a-kind quantum computing hackathon

A curated implementation of quantum algorithms with Yao.jl

This repository contains the best resources for learning practical quantum computing. This repository will be updated frequently.

QuantumFlow: A Quantum Algorithms Development Toolkit

WARNING - This package is no longer supported. EntropicaQAOA is a modular package for the quantum approximate optimisation algorithm (QAOA) built on top of Rigetti’s Forest SDK.

This repository contains implementations of Quantum Machine Learning algorithms, feature maps, variational circuits and research papers.

Library for Q# implementing various qRAM proposals

Codes for efficiently implementing quantum neural network classifiers