Machine learning on quantum experimental data toward solving quantum many-body problems.

ABSTRACT: Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds potential for unveiling obscure patterns. Although this hybrid approach extends the class of efficiently solvable problems compared to using only classical computers, this approach has been only realized for solving restricted problems because of the prevalence of noise in current quantum computers. Here, we extend the applicability of the hybrid approach to problems of interest in many-body physics, such as predicting the properties of the ground state of a given Hamiltonian and classifying quantum phases. By performing experiments with various error-reduc

SUBMITTER: Cho G 

PROVIDER: S-EPMC11364692 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature