Accelerated Inchworm Method with Tensor-Train Bath Influence Functional
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911620222943232 |
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| author | Wang, Geshuo Sun, Yixiao Yang, Siyao Cai, Zhenning |
| author_facet | Wang, Geshuo Sun, Yixiao Yang, Siyao Cai, Zhenning |
| contents | We propose an efficient tensor-train-based algorithm for simulating open quantum systems with the inchworm method, where the reduced dynamics of the open quantum system is expressed as a perturbative series of high-dimensional integrals. Instead of evaluating the integrals with Monte Carlo methods, we approximate the costly bath influence functional (BIF) in the integrand as a tensor train, allowing accurate deterministic numerical quadrature schemes implemented in an iterative manner. Thanks to the low-rank structure of the tensor train, our proposed method has a complexity that scales linearly with the number of dimensions. Our method couples seamlessly with the tensor transfer method, allowing long-time simulations of the dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerated Inchworm Method with Tensor-Train Bath Influence Functional Wang, Geshuo Sun, Yixiao Yang, Siyao Cai, Zhenning Quantum Physics Computational Physics We propose an efficient tensor-train-based algorithm for simulating open quantum systems with the inchworm method, where the reduced dynamics of the open quantum system is expressed as a perturbative series of high-dimensional integrals. Instead of evaluating the integrals with Monte Carlo methods, we approximate the costly bath influence functional (BIF) in the integrand as a tensor train, allowing accurate deterministic numerical quadrature schemes implemented in an iterative manner. Thanks to the low-rank structure of the tensor train, our proposed method has a complexity that scales linearly with the number of dimensions. Our method couples seamlessly with the tensor transfer method, allowing long-time simulations of the dynamics. |
| title | Accelerated Inchworm Method with Tensor-Train Bath Influence Functional |
| topic | Quantum Physics Computational Physics |
| url | https://arxiv.org/abs/2506.12410 |