Artificial-Intelligence-Based Surrogate Solution of Dissipative Quantum Dynamics: Physics-Informed Reconstruction of the Universal Propagator

Year: 2024

Authors: Zhang JJ., Benavides-Riveros CL., Chen LP.

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: The accurate (or even approximate) solution of the equations that govern the dynamics of dissipative quantum systems remains a challenging task in quantum science. While several algorithms have been designed to solve those equations with different degrees of flexibility, they rely mainly on highly expensive iterative schemes. Most recently, deep neural networks have been used for quantum dynamics, but current architectures are highly dependent on the physics of the particular system and usually limited to population dynamics. Here we introduce an artificial-intelligence-based surrogate model that solves dissipative quantum dynamics by parametrizing quantum propagators as Fourier neural operators, which we train using both data set and physics-informed loss functions. Compared with conventional algorithms, our quantum neural propagator avoids time-consuming iterations and provides a universal superoperator that can be used to evolve any initial quantum state for arbitrarily long times. To illustrate the wide applicability of the approach, we employ our quantum neural propagator to compute the population dynamics and time-correlation functions of the Fenna-Matthews-Olson complex.

Journal/Review: JOURNAL OF PHYSICAL CHEMISTRY LETTERS

Volume: 15 (13)      Pages from: 3603  to: 3610

More Information: J.Z. and L.P.C. acknowledge support from the starting grant of computational materials research center of Zhejiang Lab (No. 3700-32601). C.L.B.-R. gratefully acknowledges the European Union’s Horizon Europe Research and Innovation program under the Marie Sk & lstrok;odowska-Curie Grant Agreement no. 101065295-RDMFT. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Research Executive Agency.
KeyWords: Nonlinear Operators; Energy-transfer; Approximation; Coherence; Networks; System
DOI: 10.1021/acs.jpclett.4c00598

Citations: 1
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