Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
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 con...
| Authors | Yu-Cheng Lin, Yu-Chao Hsu, I-Shan Tsai et al. |
|---|---|
| Primary category | quant-ph |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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