[2502.05228] Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schrödinger Equation

Quantum Physics arXiv:2502.05228 (quant-ph) [Submitted on 6 Feb 2025 (v1), last revised 26 Mar 2026 (this version, v3)] Title:Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schrödinger Equation Authors:Kaichen Ouyang, Mingyang Yu, Zong Ke, Jun Zhang, Yi Chen, Huiling Chen View a PDF of the paper titled Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schr\"odinger Equation, by Kaichen Ouyang and 5 other authors View PDF HTML (experimental) Abstract:Physics-informed Neural Networks (PINNs) show that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of neural network solutions. Similar to optimizing loss functions in machine learning, evolutionary algorithms iteratively optimize objective functions by simulating natural selection processes. Inspired by this principle, we ask a natural question: can physical information be similarly embedded into the fitness function of evolutionary algorithms? In this work, we propose Physics-informed Evolution (PIE), a novel framework that incorporates physical information derived from governing physical laws into the evolutionary fitness landscape, thereby extending Physics-informed artificial intelligence methods from machine learning to the broader domain of evolutionary computation. As a concrete instantiation, we apply PIE to quantum control proble...