Transformer refined quantum sampling for strongly correlated electronic structure
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913159323844608 |
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| author | Zeng, Xiongzhi Gong, Ming Kan, Bowen Fan, Yi Ma, Huan Cai, Jianbin Liu, Yancheng Zhou, Naibin Jiang, Tao Guo, Shaojun Fan, Zhijie Zhang, Zongkang Li, Yuan Cao, Sirui Yan, Kai Zhu, Xiaobo Luo, Yi Shang, Honghui Li, Zhenyu Pan, Jian-Wei Yang, Jinlong |
| author_facet | Zeng, Xiongzhi Gong, Ming Kan, Bowen Fan, Yi Ma, Huan Cai, Jianbin Liu, Yancheng Zhou, Naibin Jiang, Tao Guo, Shaojun Fan, Zhijie Zhang, Zongkang Li, Yuan Cao, Sirui Yan, Kai Zhu, Xiaobo Luo, Yi Shang, Honghui Li, Zhenyu Pan, Jian-Wei Yang, Jinlong |
| contents | Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-scale quantum (NISQ) devices. Here, we introduce QiankunNet-QSCI, a hybrid quantum-classical framework that addresses this challenge by combining efficient quantum-sampling with a transformer neural network. An efficient unitary selected configuration Interaction (USCI) ansatz especially designed for quantum sampling is proposed to identify the most chemically significant electronic configurations on the Zuchongzhi 3.1 quantum processor. Subsequently, the transformer model QiankunNet learns from these sparse yet critical quantum data to infer and reconstruct the complete electronic wavefunction with high fidelity. Simulation of the challenging 40-qubit [2Fe-2S] ferredoxin active center achieves chemical accuracy. Simulation of the nitrogenase P-cluster in a 114-electron 73-orbital active space also reaches 12 milli-Hartree-level agreement with the best density matrix renormalization group (DMRG) result. QiankunNet-QSCI thus offers a practical route to accurate quantum-assisted electronic structure calculations on current devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24617 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transformer refined quantum sampling for strongly correlated electronic structure Zeng, Xiongzhi Gong, Ming Kan, Bowen Fan, Yi Ma, Huan Cai, Jianbin Liu, Yancheng Zhou, Naibin Jiang, Tao Guo, Shaojun Fan, Zhijie Zhang, Zongkang Li, Yuan Cao, Sirui Yan, Kai Zhu, Xiaobo Luo, Yi Shang, Honghui Li, Zhenyu Pan, Jian-Wei Yang, Jinlong Quantum Physics Chemical Physics Although quantum computing offers a promising solution for strongly correlated system simulation, existing algorithms face significant bottlenecks on current noisy intermediate-scale quantum (NISQ) devices. Here, we introduce QiankunNet-QSCI, a hybrid quantum-classical framework that addresses this challenge by combining efficient quantum-sampling with a transformer neural network. An efficient unitary selected configuration Interaction (USCI) ansatz especially designed for quantum sampling is proposed to identify the most chemically significant electronic configurations on the Zuchongzhi 3.1 quantum processor. Subsequently, the transformer model QiankunNet learns from these sparse yet critical quantum data to infer and reconstruct the complete electronic wavefunction with high fidelity. Simulation of the challenging 40-qubit [2Fe-2S] ferredoxin active center achieves chemical accuracy. Simulation of the nitrogenase P-cluster in a 114-electron 73-orbital active space also reaches 12 milli-Hartree-level agreement with the best density matrix renormalization group (DMRG) result. QiankunNet-QSCI thus offers a practical route to accurate quantum-assisted electronic structure calculations on current devices. |
| title | Transformer refined quantum sampling for strongly correlated electronic structure |
| topic | Quantum Physics Chemical Physics |
| url | https://arxiv.org/abs/2605.24617 |