3 papers
quant-ph2026
Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schrödinger Equation
Kaichen Ouyang, Mingyang Yu, Zong Ke +3
Physics-informed Neural Networks (PINNs) show that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of…
cs.NE2025
OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization
Junbo Jacob Lian, Mingyang Yu, Kaichen Ouyang +5
Black-box optimization often relies on evolutionary and swarm algorithms whose performance is highly problem dependent. We view an optimizer as a short program over a small vocabul…
cs.NE2025
Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN
Kaichen Ouyang, Zong Ke, Shengwei Fu +3
Evolutionary algorithms (EAs) simulate natural selection but have two main limitations: (1) they rarely update individuals based on global correlations, limiting comprehensive lear…