A JIT compiler for hybrid quantum programs in PennyLane

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

A platform-agnostic quantum runtime framework

Variational Quantum Circuits for Deep Reinforcement Learning since 2019. Xanadu Quantum Software Competition 1st Prize 2019.

Learn Quantum Machine Learning using pennylane framework

A docker container for quantum machine learning (QML) research

PennyLane/PyTorch implementation of Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning (Skolik et al., 2021)

A quantum reinforcement learning framework based on PyTorch and PennyLane.

This is an exploration using synthetic data in CSV format to apply QML models for the sake of binary classification. You can find here three different approaches. Two with Qiskit (VQC and QK/SVC) and one with Pennylane (QVC).

Project for McGill Physics Hackathon 2020

This repository implements the architecture proposed by Verdon et al. in the paper Learning to learn with quantum neural networks via classical neural networks, using PennyLane and TensorFlow.

Docker image for quantum laboratory

List of some personal QML Projects

Solutions of the QHack 2023 Quantum Coding Challenges

Qauntum convolutional neural network in protein distance prediction.

Trainable convolution for quantum-classical hybrid algorithms

This project aims to use modified layerwise learning on data re-uploading classifier to classify events in HEP. The project won second place at Xanadu's QHack Quantum Machine Learning Open Hackathon 2021.

A collection of Python samples demonstrating how to get started with IonQ using various quantum frameworks

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

Implementations of Quantum Algorithms