English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Over the past years, machine learning has emerged as a powerful computationaltool to tackle complex problems over a broad range of scientific disciplines.In particular, artificial neural networks have been successfully deployed tomitigate the exponential complexity often encountered in quantum many-bodyphysics, the study of properties of quantum systems built out of a large numberof interacting particles. In this Article, we overview some applications ofmachine learning in condensed matter physics and quantum information, withparticular emphasis on hands-on tutorials serving as a quick-start for anewcomer to the field. We present supervised machine learning withconvolutional neural networks to learn a phase transition, unsupervisedlearning with restricted Boltzmann machines to perform quantum tomography, andvariational Monte Carlo with recurrent neural-networks for approximating theground state of a many-body Hamiltonian. We briefly review the key ingredientsof each algorithm and their corresponding neural-network implementation, andshow numerical experiments for a system of interacting Rydberg atoms in twodimensions.

Neural networks in quantum many-body physics: a hands-on tutorial