{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["3(9)"],"submitter":["Chang L"],"pubmed_abstract":["Solving the ground state of quantum many-body systems remains a fundamental challenge in physics and chemistry. Recent advancements in quantum hardware have opened new avenues for addressing this challenge. Inspired by the quantum-enhanced Markov chain Monte Carlo (QeMCMC) algorithm, which was originally designed for sampling the Boltzmann distribution of classical spin models using quantum computers, we introduce a quantum-assisted variational Monte Carlo (QA-VMC) algorithm for solving the ground state of quantum many-body systems by adapting QeMCMC to sample the distribution of a (neural-network) wave function in VMC. The central question is whether such a quantum-assisted proposal can potentially offer a computational advantage over classical methods. Through numerical investigations fo"],"journal":["Precision chemistry"],"pagination":["541-553"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12458056"],"repository":["biostudies-literature"],"pubmed_title":["Quantum-Assisted Variational Monte Carlo."],"pmcid":["PMC12458056"],"pubmed_authors":["Chang L","Li Z","Fang WH"],"additional_accession":[]},"is_claimable":false,"name":"Quantum-Assisted Variational Monte Carlo.","description":"Solving the ground state of quantum many-body systems remains a fundamental challenge in physics and chemistry. Recent advancements in quantum hardware have opened new avenues for addressing this challenge. Inspired by the quantum-enhanced Markov chain Monte Carlo (QeMCMC) algorithm, which was originally designed for sampling the Boltzmann distribution of classical spin models using quantum computers, we introduce a quantum-assisted variational Monte Carlo (QA-VMC) algorithm for solving the ground state of quantum many-body systems by adapting QeMCMC to sample the distribution of a (neural-network) wave function in VMC. The central question is whether such a quantum-assisted proposal can potentially offer a computational advantage over classical methods. Through numerical investigations fo","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-03T20:22:27.933Z","creation":"2026-05-01T03:11:06.463Z"},"accession":"S-EPMC12458056","cross_references":{"pubmed":["41001091"],"doi":["10.1021/prechem.5c00025"]}}