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

arXiv:2502.05228v2 Announce Type: replace-cross
Abstract: Physics-Informed Neural Networks (PINNs) have demonstrated that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of neural network solutions. 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, bridging the long-standing connection between learning and evolution in artificial intelligence. As a concrete instantiation, we apply PIE to quantum control problems governed by the Schr”odinger 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, under varying levels of system uncertainty. Extensive comparisons against ten single-objective and five multi-objective evolutionary baselines demonstrate that PIE consistently achieves higher fidelity, lower state deviation, and improved robustness. Our results suggest that the physics-informed principle extends naturally beyond neural network training to the broader domain of evolutionary computation.