Learning Quantum Operator Dynamics from Short-Time Data

• URL: http://arxiv.org/abs/2603.14699v1
• Date: Mon, 16 Mar 2026 01:12:39 GMT
• Title: Learning Quantum Operator Dynamics from Short-Time Data
• Abstract summary: Real-time dynamics of quantum observables provide direct access to excitation spectra and correlation functions in quantum many-body systems.<n>We propose a neural ordinary differential equation (Neural ODE) framework with physics-driven designs to reconstruct long-time operator dynamics from short-time measurements.
• Score: 7.848025658827143
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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