Statistical Mechanics Calculations Using Variational Autoregressive Networks and Quantum Annealing
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911880879013888 |
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| author | Tamura, Yuta Ohzeki, Masayuki |
| author_facet | Tamura, Yuta Ohzeki, Masayuki |
| contents | In statistical mechanics, computing the partition function is generally difficult. An approximation method using a variational autoregressive network (VAN) has been proposed recently. This approach offers the advantage of directly calculating the generation probabilities while obtaining a significantly large number of samples. The present study introduces a novel approximation method that employs samples derived from quantum annealing machines in conjunction with VAN, which are empirically assumed to adhere to the Gibbs-Boltzmann distribution. When applied to the finite-size Sherrington-Kirkpatrick model, the proposed method demonstrates enhanced accuracy compared to the traditional VAN approach and other approximate methods, such as the widely utilized naive mean field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19274 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Statistical Mechanics Calculations Using Variational Autoregressive Networks and Quantum Annealing Tamura, Yuta Ohzeki, Masayuki Disordered Systems and Neural Networks Machine Learning In statistical mechanics, computing the partition function is generally difficult. An approximation method using a variational autoregressive network (VAN) has been proposed recently. This approach offers the advantage of directly calculating the generation probabilities while obtaining a significantly large number of samples. The present study introduces a novel approximation method that employs samples derived from quantum annealing machines in conjunction with VAN, which are empirically assumed to adhere to the Gibbs-Boltzmann distribution. When applied to the finite-size Sherrington-Kirkpatrick model, the proposed method demonstrates enhanced accuracy compared to the traditional VAN approach and other approximate methods, such as the widely utilized naive mean field. |
| title | Statistical Mechanics Calculations Using Variational Autoregressive Networks and Quantum Annealing |
| topic | Disordered Systems and Neural Networks Machine Learning |
| url | https://arxiv.org/abs/2404.19274 |