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/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

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

Zendy Tools

Zendy Open

Quantum machine learning algorithms, the extensions of machine learning toquantum regimes, are believed to be more powerful as they leverage the power ofquantum properties. Quantum machine learning methods have been employed tosolve quantum many-body systems and have demonstrated accurate electronicstructure calculations of lattice models, molecular systems, and recentlyperiodic systems. A hybrid approach using restricted Boltzmann machines and aquantum algorithm to obtain the probability distribution that can be optimizedclassically is a promising method due to its efficiency and ease ofimplementation. Here we implement the benchmark test of the hybrid quantummachine learning on the IBM-Q quantum computer to calculate the electronicstructure of typical 2-dimensional crystal structures: hexagonal-Boron Nitrideand graphene. The band structures of these systems calculated using the hybridquantum machine learning are in good agreement with those obtained by theconventional electronic structure calculation. This benchmark result impliesthat the hybrid quantum machine learning, empowered by quantum computers, couldprovide a new way of calculating the electronic structures of quantum many-bodysystems.

Implementation of Quantum Machine Learning for Electronic Structure Calculations of Periodic Systems on Quantum Computing Devices