AI RESEARCH
Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
arXiv CS.LG
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ArXi:2605.04604v1 Announce Type: cross High-performance computing (HPC) is increasingly important for scalable quantum chemistry workflows that couple classical generative models, quantum circuit simulation, and selected configuration interaction postprocessing. We present the generative quantum-inspired Kolmogoro-Arnold eigensolver (GQKAE), a parameter-efficient extension of the generative quantum eigensolver (GQE) for quantum chemistry.