Neural quantum propagators for driven-dissipative quantum dynamics

Year: 2025

Authors: Zhang J.J., Benavides-Riveros C.L., Chen L.P.

Autors Affiliation: Zhejiang Lab, Hangzhou 311100, Peoples R China; Univ Trento, Pitaevskii BEC Ctr, CNR INO, I-38123 Trento, Italy; Univ Trento, Dipartimento Fis, I-38123 Trento, Italy.

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.

Journal/Review: PHYSICAL REVIEW RESEARCH

Volume: 7 (1)      Pages from: L012013-1  to: L012013-8

More Information: Acknowledgments. We thank Maxim Gelin for enlightening discussions during the preparation of the manuscript. J.Z. and L.P.C. acknowledge support from the National Natural Science Foundation of China (Grant No. 22473101). C.L.B.-R. gratefully acknowledges financial support from the Royal Society of Chemistry and the European Union´s Horizon Europe Research and Innovation program under the Marie Sk\u0142odowska-Curie Grant Agreement No. 101065295-RDMFTforbosons. Views and opinions expressed are, however, those of the author only and do not necessarily reflect those of the European Union or the European Research Executive Agency.
KeyWords: Population Transfer
DOI: 10.1103/PhysRevResearch.7.L012013