Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler
The self-learning metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution. This paper provides a new self-learning Monte Carlo method...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2020-12-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.2.043442 |
| _version_ | 1797211146318512128 |
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| author | Katsuhiro Endo Taichi Nakamura Keisuke Fujii Naoki Yamamoto |
| author_facet | Katsuhiro Endo Taichi Nakamura Keisuke Fujii Naoki Yamamoto |
| author_sort | Katsuhiro Endo |
| collection | DOAJ |
| description | The self-learning metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution. This paper provides a new self-learning Monte Carlo method that utilizes a quantum computer to output a proposal distribution. In particular, we show a novel subclass of this general scheme based on the quantum Fourier transform circuit; when the dimension of the input to QFT corresponding to the low-frequency components is not large, this sampler is classically simulable while having a certain advantage over conventional methods. The performance of this quantum-inspired algorithm is demonstrated by some numerical simulations. |
| first_indexed | 2024-04-24T10:21:51Z |
| format | Article |
| id | doaj.art-9dc4084192c44c21a665f98bdbba7468 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:21:51Z |
| publishDate | 2020-12-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-9dc4084192c44c21a665f98bdbba74682024-04-12T17:05:55ZengAmerican Physical SocietyPhysical Review Research2643-15642020-12-012404344210.1103/PhysRevResearch.2.043442Quantum self-learning Monte Carlo and quantum-inspired Fourier transform samplerKatsuhiro EndoTaichi NakamuraKeisuke FujiiNaoki YamamotoThe self-learning metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution. This paper provides a new self-learning Monte Carlo method that utilizes a quantum computer to output a proposal distribution. In particular, we show a novel subclass of this general scheme based on the quantum Fourier transform circuit; when the dimension of the input to QFT corresponding to the low-frequency components is not large, this sampler is classically simulable while having a certain advantage over conventional methods. The performance of this quantum-inspired algorithm is demonstrated by some numerical simulations.http://doi.org/10.1103/PhysRevResearch.2.043442 |
| spellingShingle | Katsuhiro Endo Taichi Nakamura Keisuke Fujii Naoki Yamamoto Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler Physical Review Research |
| title | Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler |
| title_full | Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler |
| title_fullStr | Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler |
| title_full_unstemmed | Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler |
| title_short | Quantum self-learning Monte Carlo and quantum-inspired Fourier transform sampler |
| title_sort | quantum self learning monte carlo and quantum inspired fourier transform sampler |
| url | http://doi.org/10.1103/PhysRevResearch.2.043442 |
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