Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914166310174720 |
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| author | Gallego, Manuel Roca-Jerat, Sebastián Zueco, David Carrete, Jesús |
| author_facet | Gallego, Manuel Roca-Jerat, Sebastián Zueco, David Carrete, Jesús |
| contents | Machine-learning (ML) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06441 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning Gallego, Manuel Roca-Jerat, Sebastián Zueco, David Carrete, Jesús Quantum Physics Machine-learning (ML) 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. |
| title | Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2508.06441 |