{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Leng J"],"funding":["Simons Foundation","DOE | Office of Science (SC)","Simons Foundation (SF)","Heising-Simons Foundation","DOE | Office of Science","Heising-Simons Foundation (HSF)"],"pagination":["e2512789123"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12933055"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["123(8)"],"pubmed_abstract":["Sampling from probability distributions of the form [Formula: see text], where [Formula: see text] is a continuous potential, is a fundamental task across physics, chemistry, biology, computer science, and statistics. However, when [Formula: see text] is nonconvex, the resulting distribution becomes non-logconcave, and classical methods such as Langevin dynamics often exhibit poor performance. We introduce a quantum algorithm that provably accelerates a broad class of continuous-time sampling dynamics. For Langevin dynamics, our method encodes the target Gibbs measure into the amplitudes of a quantum state, identified as the kernel of a block matrix derived from a factorization of the Witten Laplacian operator. This connection enables Gibbs sampling via singular value thresholding and yiel"],"journal":["Proceedings of the National Academy of Sciences of the United States of America"],"pubmed_title":["Operator-level quantum acceleration of non-logconcave sampling."],"pmcid":["PMC12933055"],"funding_grant_id":["No. 825053","FP00010905","Quantum Systems Accelerator","Quantum Utility through Advanced Computational Quantum Algorithms No. DE-SC0025572","Quantum Systems Accelerators"],"pubmed_authors":["Chen Z","Ding Z","Lin L","Leng J"],"additional_accession":[]},"is_claimable":false,"name":"Operator-level quantum acceleration of non-logconcave sampling.","description":"Sampling from probability distributions of the form [Formula: see text], where [Formula: see text] is a continuous potential, is a fundamental task across physics, chemistry, biology, computer science, and statistics. However, when [Formula: see text] is nonconvex, the resulting distribution becomes non-logconcave, and classical methods such as Langevin dynamics often exhibit poor performance. We introduce a quantum algorithm that provably accelerates a broad class of continuous-time sampling dynamics. For Langevin dynamics, our method encodes the target Gibbs measure into the amplitudes of a quantum state, identified as the kernel of a block matrix derived from a factorization of the Witten Laplacian operator. This connection enables Gibbs sampling via singular value thresholding and yiel","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-16T20:45:47.324Z","creation":"2026-07-10T03:11:56.09Z"},"accession":"S-EPMC12933055","cross_references":{"pubmed":["41719333"],"doi":["10.1073/pnas.2512789123"]}}