Learning Quantum Operator Dynamics from Short-Time Data

Jinyang Li; Satoshi Iso; Shunji Matsuura; Lingxiao Wang; Xiaoyang Wang

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arXiv · 2603.14699

Learning Quantum Operator Dynamics from Short-Time Data

Jinyang Li·Satoshi Iso·Shunji Matsuura·Lingxiao Wang·Xiaoyang Wang

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Abstract

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.

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BibTeX RIS

Jinyang Li, Satoshi Iso, Shunji Matsuura, Lingxiao Wang, Xiaoyang Wang. 2026-03-16. Learning Quantum Operator Dynamics from Short-Time Data. https://arxiv.org/abs/2603.14699

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