PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
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.
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".
This repository contains the source code used to produce the results presented in the paper "Near-deterministic production of universal quantum photonic gates enhanced by machine learning".
Expected behavior
Si=σxRY(cos−1((i−2)/2)))σx and Si=RY(−cos−1((i−2)/2))) should be Si=σxRY(cos−1((i−2)/i)))σx and Si=RY(−cos−1((i−2)/i))) respectively.
This repository contains the best resources(video links, books, jupyter notebooks) for learning practical quantum computing. This repository will be updated frequently. Make sure you star and watch this repository to stay updated.
Implementation of an algorithm for training Quantum Boltzmann Machine neural networks using variational methods. Based on https://arxiv.org/abs/1712.05304 and their sample code.
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.
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