Diffusion Models as Stochastic Quantization in Lattice Field Theory
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911870810587136 |
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| author | Wang, Lingxiao Aarts, Gert Zhou, Kai |
| author_facet | Wang, Lingxiao Aarts, Gert Zhou, Kai |
| contents | In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ). The DM is realized by approximating the reversal of a stochastic process dictated by the Langevin equation, generating samples from a prior distribution to effectively mimic the target distribution. Using numerical simulations, we demonstrate that the DM can serve as a global sampler for generating quantum lattice field configurations in two-dimensional $ϕ^4$ theory. We demonstrate that DMs can notably reduce autocorrelation times in the Markov chain, especially in the critical region where standard Markov Chain Monte-Carlo (MCMC) algorithms experience critical slowing down. The findings can potentially inspire further advancements in lattice field theory simulations, in particular in cases where it is expensive to generate large ensembles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_17082 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Diffusion Models as Stochastic Quantization in Lattice Field Theory Wang, Lingxiao Aarts, Gert Zhou, Kai High Energy Physics - Lattice Machine Learning In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ). The DM is realized by approximating the reversal of a stochastic process dictated by the Langevin equation, generating samples from a prior distribution to effectively mimic the target distribution. Using numerical simulations, we demonstrate that the DM can serve as a global sampler for generating quantum lattice field configurations in two-dimensional $ϕ^4$ theory. We demonstrate that DMs can notably reduce autocorrelation times in the Markov chain, especially in the critical region where standard Markov Chain Monte-Carlo (MCMC) algorithms experience critical slowing down. The findings can potentially inspire further advancements in lattice field theory simulations, in particular in cases where it is expensive to generate large ensembles. |
| title | Diffusion Models as Stochastic Quantization in Lattice Field Theory |
| topic | High Energy Physics - Lattice Machine Learning |
| url | https://arxiv.org/abs/2309.17082 |