Cirq/PyTorch implementation of Quantum Architecture Search via Deep Reinforcement Learning by (Kuo et al., 2021)

Ground States of Quantum Many Body Lattice Models via Reinforcement Learning

Roadmap to becoming an Artificial Intelligence Expert in 2020

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

A university quantum algorithms/computation course supplement based on Qiskit

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

The code is for optimizing a unitary tensor-network operator.

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Examples for QuantumOptics.jl

In this tutorial we interactively design and explore a reinforcement learning environment for ion trap quantum computers.

OpenAI gym environment for the design of ion trap experiments generating high-dimensional multipartite entanglement.

Course on topology in condensed matter

Object-oriented Python library for computation of properties of highly-excited Rydbeg states of alkali atoms.

PyTorch implementations of Generative Adversarial Networks.

Lectures on scientific computing with python, as IPython notebooks.

Predict and analyze cellular automata using convolutional neural networks

scikit-learn: machine learning in Python

Image processing in Python

Automated Machine Learning with scikit-learn

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Deep Learning for humans

QuTiP: Quantum Toolbox in Python

Global search algorithms for finding optimal tensor network contraction sequences.

Tensor network contraction function for Python 3.