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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.04604 |
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| _version_ | 1866917463589912576 |
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| author | Lin, Yu-Cheng Hsu, Yu-Chao Tsai, I-Shan Lin, Chun-Hua Peng, Kuo-Chung Jiang, Jiun-Cheng Wang, Yun-Yuan Huang, Tzung-Chi Li, Tai-Yue Chen, Kuan-Cheng Chen, Samuel Yen-Chi Chen, Nan-Yow |
| author_facet | Lin, Yu-Cheng Hsu, Yu-Chao Tsai, I-Shan Lin, Chun-Hua Peng, Kuo-Chung Jiang, Jiun-Cheng Wang, Yun-Yuan Huang, Tzung-Chi Li, Tai-Yue Chen, Kuan-Cheng Chen, Samuel Yen-Chi Chen, Nan-Yow |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04604 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative Quantum-inspired Kolmogorov-Arnold Eigensolver Lin, Yu-Cheng Hsu, Yu-Chao Tsai, I-Shan Lin, Chun-Hua Peng, Kuo-Chung Jiang, Jiun-Cheng Wang, Yun-Yuan Huang, Tzung-Chi Li, Tai-Yue Chen, Kuan-Cheng Chen, Samuel Yen-Chi Chen, Nan-Yow Quantum Physics Machine Learning 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. |
| title | Generative Quantum-inspired Kolmogorov-Arnold Eigensolver |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2605.04604 |