Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911629837336576 |
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| author | Cao, Long Ge, Liwei Zhang, Daochi Wang, Yao Xu, Rui-Xue Yan, YiJing Zheng, Xiao |
| author_facet | Cao, Long Ge, Liwei Zhang, Daochi Wang, Yao Xu, Rui-Xue Yan, YiJing Zheng, Xiao |
| contents | This work integrates the physics-informed neural network (PINN) approach into the neural quantum state framework to simulate open quantum system dynamics, to circumvent the computationally expensive time-dependent variational principle required in conventional variational methods. The proposed PINN-DQME method employs time-encoded neural networks within a time-domain decomposition strategy to represent the evolution governed by the dissipaton-embedded quantum master equation (DQME). We implement and validate this approach in the single-impurity Anderson model, benchmarking the PINN-DQME results against the numerically exact hierarchical equations of motion. The PINN-DQME method demonstrates high accuracy in simulating quantum dissipative dynamics at high temperatures, where non-Markovian effects are weak. However, for strongly non-Markovian dynamics at low temperatures, it encounters challenges with error accumulation during time propagation, highlighting an area for future refinement in applying PINNs to complex quantum dynamical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08081 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network Cao, Long Ge, Liwei Zhang, Daochi Wang, Yao Xu, Rui-Xue Yan, YiJing Zheng, Xiao Quantum Physics This work integrates the physics-informed neural network (PINN) approach into the neural quantum state framework to simulate open quantum system dynamics, to circumvent the computationally expensive time-dependent variational principle required in conventional variational methods. The proposed PINN-DQME method employs time-encoded neural networks within a time-domain decomposition strategy to represent the evolution governed by the dissipaton-embedded quantum master equation (DQME). We implement and validate this approach in the single-impurity Anderson model, benchmarking the PINN-DQME results against the numerically exact hierarchical equations of motion. The PINN-DQME method demonstrates high accuracy in simulating quantum dissipative dynamics at high temperatures, where non-Markovian effects are weak. However, for strongly non-Markovian dynamics at low temperatures, it encounters challenges with error accumulation during time propagation, highlighting an area for future refinement in applying PINNs to complex quantum dynamical settings. |
| title | Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2603.08081 |