{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:52:09Z","timestamp":1774925529483,"version":"3.50.1"},"reference-count":26,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T00:00:00Z","timestamp":1687478400000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,23]],"date-time":"2023-06-23T00:00:00Z","timestamp":1687478400000},"content-version":"tdm","delay-in-days":22,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"National Science Foundation","award":["DMS 2038030"],"award-info":[{"award-number":["DMS 2038030"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems. Despite rapid developments, significant scalability challenges arise when considering molecules of large scale, which correspond to non-locally interacting quantum spin Hamiltonians consisting of sums of thousands or even millions of Pauli operators. In this work, we introduce scalable parallelization strategies to improve neural-network-based variational quantum Monte Carlo calculations for <jats:italic>ab-initio<\/jats:italic> quantum chemistry applications. We establish GPU-supported local energy parallelism to compute the optimization objective for Hamiltonians of potentially complex molecules. Using autoregressive sampling techniques, we demonstrate systematic improvement in wall-clock timings required to achieve coupled cluster with up to double excitations baseline target energies. The performance is further enhanced by accommodating the structure of resultant spin Hamiltonians into the autoregressive sampling ordering. The algorithm achieves promising performance in comparison with the classical approximate methods and exhibits both running time and scalability advantages over existing neural-network based methods.<\/jats:p>","DOI":"10.1088\/2632-2153\/acdb2f","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T22:57:01Z","timestamp":1685746621000},"page":"025034","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Scalable neural quantum states architecture for quantum chemistry"],"prefix":"10.1088","volume":"4","author":[{"given":"Tianchen","family":"Zhao","sequence":"first","affiliation":[]},{"given":"James","family":"Stokes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2294-7233","authenticated-orcid":true,"given":"Shravan","family":"Veerapaneni","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2023,6,23]]},"reference":[{"key":"mlstacdb2fbib1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.94.170201","article-title":"Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations","volume":"94","author":"Troyer","year":"2005","journal-title":"Phys. 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