Solving equations that govern dissipative quantum systems is a complex task. However, an artificial intelligence-based surrogate model has been introduced to simplify this process. The model uses Fourier neural operators to parameterize quantum propagators, trained using dataset and physics-informed loss functions. This approach eliminates the need for time-consuming iterations and provides a universal super-operator that can evolve any initial quantum state arbitrarily long time. The model has been tested on the Fenna-Matthews-Olson complex, a pigment-protein complex found in green sulfur bacteria, demonstrating its potential for improving simulation methods.
What is the Challenge in Solving Equations Governing Dissipative Quantum Systems?
The accurate or approximate solution of the equations that govern the dynamics of dissipative quantum systems remains a challenging task for quantum science. Several algorithms have been designed to solve these equations with varying degrees of flexibility, but they primarily rely on highly expensive iterative schemes. Recently, deep neural networks have been used for quantum dynamics, but current architectures are highly dependent on the physics of the particular system and are usually limited to population dynamics.
How Does Artificial Intelligence Help in Quantum Dynamics?
An artificial intelligence-based surrogate model that solves dissipative quantum dynamics by parameterizing quantum propagators as Fourier neural operators has been introduced. These operators are trained using both dataset and physics-informed loss functions. Compared with conventional algorithms, this quantum neural propagator avoids time-consuming iterations and provides a universal super-operator that can evolve any initial quantum state for arbitrarily long times.
What is the Role of Fourier Neural Operators in Quantum Dynamics?
Fourier neural operators play a crucial role in the artificial intelligence-based surrogate model. They are used to parameterize the quantum propagators, which are then trained using both dataset and physics-informed loss functions. This versatile approach can be easily extended to solve other equations of motion for quantum dynamics. The trained propagator allows for the direct computation of dynamics up to a chosen time limit for any initial state through a single-step operation, eliminating the need for tedious, expensive iterations.
How Does the AI-based Surrogate Model Improve Quantum Dynamics?
The AI-based surrogate model improves quantum dynamics by providing a universal super-operator that can evolve any initial quantum state for arbitrarily long times. This is achieved by parameterizing quantum propagators as Fourier neural operators and training them using both dataset and physics-informed loss functions. The method can easily be extended to arbitrarily long times and can be used to compute the more challenging time-correlation functions of system operators.
How is the AI-based Surrogate Model Tested?
The AI-based surrogate model is tested by training a neural operator as the quantum propagator of the well-known Fenna-Matthews-Olson complex, computing population dynamics, and multi-time correlation functions of system operators. The Fenna-Matthews-Olson complex is a pigment-protein complex found in green sulfur bacteria, and its interplay between molecular excitations, environment interaction, and quantum coherence effects makes it an important workhorse for developing and improving simulation methods.
In the article titled “Artificial-intelligence-based surrogate solution of dissipative quantum dynamics: physics-informed reconstruction of the universal propagator“, authors Jiaji Zhang, Carlos L. Benavides-Riveros, and Lipeng Chen explore the application of artificial intelligence in the field of quantum dynamics. The paper, published on February 5, 2024, presents a novel approach to the surrogate solution of dissipative quantum dynamics using AI. The research was published in arXiv, a repository of electronic preprints approved for publication after moderation, hosted by Cornell University.
