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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.14699 |
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| _version_ | 1866918389901950976 |
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| author | Li, Jinyang Iso, Satoshi Matsuura, Shunji Wang, Lingxiao Wang, Xiaoyang |
| author_facet | Li, Jinyang Iso, Satoshi Matsuura, Shunji Wang, Lingxiao Wang, Xiaoyang |
| contents | Real-time dynamics of quantum observables provide direct access to excitation spectra and correlation functions in quantum many-body systems, but currently available quantum devices are limited to short evolution times due to decoherence. We propose a neural ordinary differential equation (Neural ODE) framework with physics-driven designs to reconstruct long-time operator dynamics from short-time measurements. By expanding observables in the Pauli basis and exploiting locality and symmetry constraints, the operator evolution is reduced to a tractable set of coefficients whose dynamics are learned from data. Applied to the transverse-field Ising model, the method accurately extrapolates long-time behavior and resolves excitation spectra from noisy short-time signals. Our results demonstrate a scalable and data-efficient strategy for extracting dynamical and spectral information from practical quantum hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14699 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning Quantum Operator Dynamics from Short-Time Data Li, Jinyang Iso, Satoshi Matsuura, Shunji Wang, Lingxiao Wang, Xiaoyang Quantum Physics Real-time dynamics of quantum observables provide direct access to excitation spectra and correlation functions in quantum many-body systems, but currently available quantum devices are limited to short evolution times due to decoherence. We propose a neural ordinary differential equation (Neural ODE) framework with physics-driven designs to reconstruct long-time operator dynamics from short-time measurements. By expanding observables in the Pauli basis and exploiting locality and symmetry constraints, the operator evolution is reduced to a tractable set of coefficients whose dynamics are learned from data. Applied to the transverse-field Ising model, the method accurately extrapolates long-time behavior and resolves excitation spectra from noisy short-time signals. Our results demonstrate a scalable and data-efficient strategy for extracting dynamical and spectral information from practical quantum hardware. |
| title | Learning Quantum Operator Dynamics from Short-Time Data |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2603.14699 |