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Fourier Neural Operators for Time-Periodic Quantum Systems: Learning Floquet Hamiltonians, Observable Dynamics, and Operator Growth

Zihao Qi1,*, Yang Peng2,3,†, and Christopher Earls4,‡

  • *Contact author: zq73@cornell.edu
  • †Contact author: yang.peng@csun.edu
  • ‡Contact author: earls@cornell.edu
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PRX Quantum 7, 020340 – Published 28 May, 2026

DOI: https://doi.org/10.1103/2tt3-yndr

Abstract

Time-periodic quantum systems exhibit a rich variety of far-from-equilibrium phenomena and serve as ideal platforms for quantum engineering and control. However, simulating their dynamics with conventional numerical methods remains challenging due to the exponential growth of Hilbert space dimension and rapid spreading of entanglement. In this work, we introduce Fourier neural operators (FNOs) as an efficient, accurate, and scalable framework for nonequilibrium quantum dynamics. Parameterized in Fourier space, FNO naturally captures temporal correlations and remains minimally dependent on discretization of time. We demonstrate the versatility of FNO through three complementary learning paradigms: reconstructing effective Floquet Hamiltonians, predicting expectation values of local observables, and learning quantum information spreading. For each learning task, FNO achieves remarkable accuracy, while attaining a significant speedup, compared to exact numerical methods. Moreover, FNO possesses capabilities beyond that of conventional methods, such as predicting all local observables from a subset of measurements without information about the Hamiltonian, as well as extrapolating beyond the time window provided by training data, enabling access to observables and operator-spreading dynamics that might be beyond the coherence time. By employing a spatially local basis, we argue that the computational cost of FNOs scales only polynomially with the system size. Our results establish FNO as a versatile and scalable computation framework that integrates numerical simulations and experimental data seamlessly, with direct implications for extracting meaningful physics from measurements by near-term quantum computers.

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Popular Summary

Driven quantum systems host many exotic phenomena far from equilibrium, but simulating their real-time dynamics is notoriously difficult. Recent advances in quantum simulators provide access to high-fidelity experimental data, but such measurements are typically limited to short times.

To bridge this gap between simulation and experiment, we introduce neural operator learning as a data-driven paradigm for time-periodic quantum systems. By working with local observables, our method keeps the computational cost polynomial in system size; it also enables evaluations of expectation values hundreds of times faster than conventional numerical methods. Once trained, the model transfers reliably across driving frequencies, system parameters, and time discretizations. Our approach also extrapolates beyond the training window to predict dynamics past experimental times. Our method provides a data-centered route to exploring nonequilibrium physics at system sizes beyond the reach of classical numerical methods.

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PRX Quantum 7, 020340– Published 28 May, 2026
  • Received 11 September 2025
  • Revised 14 February 2026
  • Accepted 13 April 2026
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