Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schr\"odinger Equation

TL;DR

This paper introduces Physics-informed Evolution (PIE), a novel framework that embeds physical laws into evolutionary algorithms to improve quantum control solutions, demonstrating enhanced performance across multiple quantum benchmarks.

Contribution

The paper extends physics-informed principles from neural networks to evolutionary algorithms, proposing a new framework for quantum control problems governed by the Schrödinger equation.

Findings

PIE effectively guides evolutionary search towards high-fidelity control fields.

Embedding physical information improves robustness and performance across benchmarks.

Systematic comparison shows PIE's superiority over traditional evolutionary methods.

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 problems governed by the Schr\"{o}dinger equation, where the goal is to find optimal control fields that drive quantum systems from initial states to desired target states. We validate PIE on three representative quantum control benchmarks: state preparation in V-type three-level systems, entangled state generation in superconducting quantum circuits, and two-atom cavity QED systems. Within the PIE framework, we systematically compare the performance of ten single-objective and five multi-objective evolutionary algorithms. Experimental results demonstrate that by embedding physical information into the fitness function, PIE effectively guides evolutionary search, yielding control fields with high fidelity, low state deviation, and robust performance across different scenarios. Our findings further suggest that the Physics-informed principle extends naturally beyond neural network training to the broader domain of evolutionary computation.