Neural network solution of non-Markovian quantum state diffusion and operator construction of quantum stochastic process

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; IQM Quantum Comp, Georg Brauchle Ring 23-25, D-80992 Munich, Germany.

Abstract: Non-Markovian quantum state diffusion provides a wavefunction-based framework for modeling open quantum systems. In this work, we introduce a novel machine learning approach based on an operator construction algorithm. This algorithm employs a neural network as a universal generator to reconstruct the stochastic time evolution operator from an ensemble of quantum trajectories. Unlike conventional machine learning methods that approximate time-dependent wavefunctions or expectation values, our operator-based approach offers broader applicability to stochastic processes. We benchmark the algorithm on the spin-boson model across diverse spectral densities, demonstrating its accuracy. Furthermore, we showcase the operator’s utility in calculating absorption spectra and reconstructing reduced density matrices at extended timescales. These results establish a new paradigm for the application of machine learning in quantum dynamics.

Journal/Review: JOURNAL OF CHEMICAL PHYSICS

Volume: 163 (19)      Pages from: 194103-1  to: 194103-10

More Information: J.Z. and L.C. acknowledge the support from the National Natural Science Foundation of China (Grant No. 22473101). C.L.B.R. gratefully acknowledges the financial support from the Royal Society of Chemistry and the European Union’s Horizon Europe Research and Innovation program under the Marie Sk & lstrok;odowska-Curie Grant Agreement No. 101065295-RDMFTforbosons.
KeyWords: Universal Approximation; Multidimensional Spectroscopy; Vibrational Spectroscopy; Nonlinear Operators; Dynamics; System
DOI: 10.1063/5.0298594