Accelerating quantum Monte Carlo calculations with set-equivariant architectures and transfer learning

Gallego, Manuel (Universidad de Zaragoza) ; Roca-Jerat, Sebastián ; Zueco, David ; Carrete, Jesús

Resumen: 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.

Idioma: Inglés

DOI: 10.1088/2632-2153/ae29bb

Año: 2025

Publicado en: Machine Learning: Science and Technology 6, 4 (2025), 045070 [13 pp.]
ISSN: 2632-2153

Financiación: info:eu-repo/grantAgreement/ES/MICIU/CEX2023-001286-S
Financiación: info:eu-repo/grantAgreement/ES/MICIU/PRTR-C17.I1
Financiación: info:eu-repo/grantAgreement/ES/MICIU/AEI/10.13039/501100011033

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Física Teórica (Dpto. Física Teórica)