Simulating Non-Markovian Open Quantum Dynamics with Neural Quantum States
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914378814586880 |
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| author | Cao, Long Ge, Liwei Zhang, Daochi Li, Xiang Wang, Yao Xu, Rui-Xue Yan, YiJing Zheng, Xiao |
| author_facet | Cao, Long Ge, Liwei Zhang, Daochi Li, Xiang Wang, Yao Xu, Rui-Xue Yan, YiJing Zheng, Xiao |
| contents | Reducing computational scaling for simulating non-Markovian dissipative dynamics using artificial neural networks is both a major focus and formidable challenge in open quantum systems. To enable neural quantum states (NQSs), we encode environmental memory in dissipatons (quasiparticles with characteristic lifetimes), yielding the dissipaton-embedded quantum master equation (DQME). The resulting NQS-DQME framework achieves compact representation of many-body correlations and non-Markovian memory. Benchmarking against numerically exact hierarchical equations of motion confirms NQS-DQME maintains comparable accuracy while enhancing scalability and interpretability. This methodology opens new paths to explore non-Markovian open quantum dynamics in previously intractable systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11093 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Simulating Non-Markovian Open Quantum Dynamics with Neural Quantum States Cao, Long Ge, Liwei Zhang, Daochi Li, Xiang Wang, Yao Xu, Rui-Xue Yan, YiJing Zheng, Xiao Quantum Physics Machine Learning Reducing computational scaling for simulating non-Markovian dissipative dynamics using artificial neural networks is both a major focus and formidable challenge in open quantum systems. To enable neural quantum states (NQSs), we encode environmental memory in dissipatons (quasiparticles with characteristic lifetimes), yielding the dissipaton-embedded quantum master equation (DQME). The resulting NQS-DQME framework achieves compact representation of many-body correlations and non-Markovian memory. Benchmarking against numerically exact hierarchical equations of motion confirms NQS-DQME maintains comparable accuracy while enhancing scalability and interpretability. This methodology opens new paths to explore non-Markovian open quantum dynamics in previously intractable systems. |
| title | Simulating Non-Markovian Open Quantum Dynamics with Neural Quantum States |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2404.11093 |