A JIT compiler for hybrid quantum programs in PennyLane
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Updated
May 25, 2024 - Python
A JIT compiler for hybrid quantum programs in PennyLane
Variational Quantum Circuits for Deep Reinforcement Learning since 2019. Xanadu Quantum Software Competition 1st Prize 2019.
A platform-agnostic quantum runtime framework
A docker container for quantum machine learning (QML) research
IEEE ICASSP 21 - Quantum Convolution Neural Networks for Speech Processing and Automatic Speech Recognition
Learn Quantum Machine Learning using pennylane framework
PennyLane/PyTorch implementation of Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning (Skolik et al., 2021)
Trainable convolution for quantum-classical hybrid algorithms
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).
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.
Docker image for quantum laboratory
Some quantum experiments
A collection of Python samples demonstrating how to get started with IonQ using various quantum frameworks
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.
Qauntum convolutional neural network in protein distance prediction.
Quant'ronauts repository for QHACK21
Materials and Resources for the EuroPython 21 Talk on "Introduction to Quantum Deep Learning" on 28/7/2021.
Creating Variational Quantum Algorithm from scratch to find optimal portfolios
Quantum Computing Notes | Quantum algorithms and protocols using qiskit | QFT using Pennylane and Cirq | Xanadu CodeBook | Tutorials in Cirq
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