165615 20260112132217.0 doi 10.1088/2632-2153/ae29bb sideral 147320 ART-2025-147320 eng Gallego, Manuel Universidad de Zaragoza Accelerating quantum Monte Carlo calculations with set-equivariant architectures and transfer learning 2025 Machine-learning ansätze have greatly expanded the accuracy and reach of variational quantum Monte Carlo (QMC) calculations, in particular when exploring the manifold quantum phenomena exhibited by spin systems. However, the scalability of QMC is still compromised by several other bottlenecks, and specifically those related to the actual evaluation of observables based on random deviates that lies at the core of the approach. Here we show how the set-transformer architecture can be used to dramatically accelerate or even bypass that step, especially for time-consuming operators such as powers of the magnetization. We illustrate the procedure with a range of examples structured around quantum spin systems with long-range interactions, and comprising both regressions (to predict observables) and classifications (to detect phase transitions). Moreover, we show how transfer learning can be leveraged to reduce the training cost by reusing knowledge from different systems and smaller system sizes. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/MICIU/CEX2023-001286-S info:eu-repo/grantAgreement/ES/MICIU/PRTR-C17.I1 info:eu-repo/grantAgreement/ES/MICIU/AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Roca-Jerat, Sebastián Zueco, David (orcid)0000-0003-4478-1948 Carrete, Jesús (orcid)0000-0003-0971-1098 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 6, 4 (2025), 045070 [13 pp.] Machine Learning: Science and Technology 2632-2153 1104684 http://zaguan.unizar.es/record/165615/files/texto_completo.pdf Versión publicada 751369 http://zaguan.unizar.es/record/165615/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:165615 articulos driver 2026-01-12-11:11:14 ARTICLE