Artificial-intelligence-based surrogate solution of dissipative quantum
dynamics: physics-informed reconstruction of the universal propagator
- URL: http://arxiv.org/abs/2402.02788v1
- Date: Mon, 5 Feb 2024 07:52:04 GMT
- Title: Artificial-intelligence-based surrogate solution of dissipative quantum
dynamics: physics-informed reconstruction of the universal propagator
- Authors: Jiaji Zhang, Carlos L. Benavides-Riveros, Lipeng Chen
- Abstract summary: We introduce an artificial-intelligence-based surrogate model that solves dissipative quantum dynamics.
Our quantum neural propagator avoids time-consuming iterations and provides a universal super-operator.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The accurate (or even approximate) solution of the equations that govern the
dynamics of dissipative quantum systems remains a challenging task for 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 parameterizing quantum propagators as Fourier
neural operators, which we train using both dataset and physics-informed loss
functions. Compared with conventional algorithms, our quantum neural propagator
avoids time-consuming iterations and provides a universal super-operator 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 population dynamics and time-correlation functions of the
Fenna-Matthews-Olson complex.
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