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

Inspired by the recent work of Carleo and Troyer[1], we apply machinelearning methods to quantum mechanics in this article. The radial basisfunction network in a discrete basis is used as the variational wavefunctionfor the ground state of a quantum system. Variational Monte Carlo(VMC)calculations are carried out for some simple Hamiltonians. The results are ingood agreements with theoretical values. The smallest eigenvalue of a Hermitianmatrix can also be acquired using VMC calculations. Our results demonstratethat machine learning techniques are capable of solving quantum mechanicalproblems.

Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks