Organizations: Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. · National Center for High-Performance Computing, National Institutes of Applied Research, Hsinchu, Taiwan · Cross College Elite Program, National Cheng Kung University, Tainan, Taiwan · Department of Mathematics, University of California, San Diego, San Diego, California, USA · Department of Physics and Center for Theoretical Physics, National Taiwan University, Taipei, Taiwan · NVIDIA AI Technology Center, NVIDIA Corp., Taipei, Taiwan · Center for Quantum Science and Engineering, National Taiwan University, Taipei, Taiwan · Department of Electrical and Electronic Engineering, Imperial College London, London, UK · Centre for Quantum Engineering, Science and Technology, Imperial College London, London, UK · Wells Fargo, New York, NY, USA
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 Kolmogorov-Arnold eigensolver (GQKAE), a parameter-efficient extension of the generative quantum eigensolver (GQE) for quantum chemistry. GQKAE replaces the parameter-heavy feed-forward network components in GPT-style generative eigensolvers with hybrid quantum-inspired Kolmogorov-Arnold network modules, forming a compact HQKANsformer backbone. The method preserves autoregressive operator selection and the quantum-selected configuration interaction evaluation pipeline, while using single-qubit DatA Re-Uploading ActivatioN modules to provide expressive nonlinear mappings. Numerical benchmarks on H4, N2, LiH, C2H6, H2O, and the H2O dimer show that GQKAE achieves chemical accuracy comparable to the GPT-based GQE architecture, while reducing trainable parameters and memory by approximately 66% and improving wall-time performance. For strongly correlated systems such as N2 and LiH, GQKAE also improves convergence behavior and final energy errors. These results indicate that quantum-inspired Kolmogorov-Arnold networks can reduce classical-side overhead while preserving circuit-generation quality, offering a scalable route for HPC-quantum co-design on near-term quantum platforms.