| title | Generative Quantum-inspired Kolmogorov-Arnold Eigensolver |
|---|---|
| authors | 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 |
| date | 2026-05-06 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.04604 |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| 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 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. |
cite
@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},
eprint={2605.04604},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.04604}
}