# NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems

## TL;DR

NetKet is an open-source toolkit that leverages machine learning, specifically neural-network quantum states, to facilitate research in many-body quantum systems, including state tomography and ground state searches.

## Contribution

It introduces a flexible framework for neural-network quantum states and algorithms for key quantum physics tasks, fostering collaboration at the intersection of machine learning and many-body physics.

## Key findings

- Provides a versatile platform for quantum state tomography
- Enables supervised learning from wave-function data
- Supports ground state searches for various lattice models

## Abstract

We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wave functions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wave-function data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics.