Paper Detail
Yu-Cheng Lin, Yu-Chao Hsu, I-Shan Tsai, Chun-Hua Lin, Kuo-Chung Peng, Jiun-Cheng Jiang, Yun-Yuan Wang, Tzung-Chi Huang, Tai-Yue Li, Kuan-Cheng Chen, Samuel Yen-Chi Chen, Nan-Yow Chen
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.
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@misc{lin2026generative,
title = {Generative Quantum-inspired Kolmogorov-Arnold Eigensolver},
author = {Yu-Cheng Lin and Yu-Chao Hsu and I-Shan Tsai and Chun-Hua Lin and Kuo-Chung Peng and Jiun-Cheng Jiang and Yun-Yuan Wang and Tzung-Chi Huang and Tai-Yue Li and Kuan-Cheng Chen and Samuel Yen-Chi Chen and Nan-Yow Chen},
year = {2026},
abstract = {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 eige},
url = {https://huggingface.co/papers/2605.04604},
keywords = {generative quantum eigensolver, Kolmogorov-Arnold network modules, HQKANsformer, quantum-selected configuration interaction, autoregressive operator selection, single-qubit DatA Re-Uploading ActivatioN modules, quantum circuit simulation, parameter-efficient extension, near-term quantum platforms, huggingface daily},
eprint = {2605.04604},
archiveprefix = {arXiv},
}
{}