Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum
physics. NetKet is built around neural-network quantum states and provides efficient …

In experimentally realistic situations, quantum systems are never perfectly isolated and the
coupling to their environment needs to be taken into account. Often, the effect of the …

Neural-network quantum states have been successfully used to study a variety of lattice and
continuous-space problems. Despite a great deal of general methodological developments …

The efficient numerical simulation of nonequilibrium real-time evolution in isolated quantum
matter constitutes a key challenge for current computational methods. This holds in …

Restricted Boltzmann machines in quantum physics | Nature Physics Skip to main content Thank
you for visiting nature.com. You are using a browser version with limited support for CSS. To …

We present a general variational approach to determine the steady state of open quantum
lattice systems via a neural-network approach. The steady-state density matrix of the lattice …

Neural quantum states are a new family of variational Ansätze for quantum-many body wave
functions with advantageous properties in the notoriously challenging case of two spatial …

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 …