Clifford circuits, graph states, and other quantum Stabilizer formalism tools.

Deep learning quantum Monte Carlo for electrons in real space

Manifold-learning flows (ℳ-flows)

A command-line benchmarking tool

Trax — Deep Learning with Clear Code and Speed

Express & compile probabilistic programs for performant inference on CPU & GPU. Powered by JAX.

Just a little MCMC

Code to reproduce the results of 👇

Bayesian Modeling and Probabilistic Programming in Python

Variational Monte Carlo with generative flows for mini-BMN matrix models

Learn Julia via interactive tutorials!

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

JAX-based neural network library

Implementation of normalising flows and constrained random variable transformations

The Julia Programming Language

Code for 'Solving Statistical Mechanics using Variational Autoregressive Networks'.

A curated implementation of quantum algorithms with Yao.jl

An open-source Python framework for hybrid quantum-classical machine learning.

Linux daemon for Intel CPU power limits and firmware-induced throttling.

A wavefunction ansatz based on Recurrent Neural Networks to perform Variational Monte-Carlo Simulations

Using very few measurements to predict properties in quantum many-body systems.

autodiff compiler

Discrete Truncated Wigner Approximation for any spin system

Flax is a neural network library for JAX that is designed for flexibility.

Normalizing flows in PyTorch. Current intended use is education not production.