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  • Letter
  • Open Access

Neural quantum propagators for driven-dissipative quantum dynamics

Jiaji Zhang1, Carlos L. Benavides-Riveros2,*, and Lipeng Chen1,†

  • *Contact author: cl.benavidesriveros@unitn.it
  • †Contact author: chenlp@zhejianglab.com
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Phys. Rev. Research 7, L012013 – Published 21 January, 2025

DOI: https://doi.org/10.1103/PhysRevResearch.7.L012013

Abstract

Describing the dynamics of strong-laser driven open quantum systems is a very challenging task that requires the solution of highly involved equations of motion. While machine learning techniques are being applied with some success to simulate the time evolution of individual quantum states, their use to approximate time-dependent operators (that can evolve various states) remains largely unexplored. In this work, we develop driven neural quantum propagators (NQP), a universal neural network framework that solves driven-dissipative quantum dynamics by approximating propagators rather than wave functions or density matrices. NQP can handle arbitrary initial quantum states, adapt to various external fields, and simulate long-time dynamics, even when trained on far shorter time windows. Furthermore, by appropriately configuring the external fields, our trained NQP can be transferred to systems governed by different Hamiltonians. We demonstrate the effectiveness of our approach by studying the spin-boson and the three-state transition Gamma models.

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Phys. Rev. Research 7, L012013– Published 21 January, 2025
  • Received 22 October 2024
  • Accepted 20 December 2024
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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