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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.06347 |
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| _version_ | 1866912705970962432 |
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| author | Cao, Qing-Hong Hou, Zong-Yue Li, Ying-Ying Liu, Xiaohui Song, Zhuo-Yang Zhang, Liang-Qi Zhang, Shutao Zhao, Ke |
| author_facet | Cao, Qing-Hong Hou, Zong-Yue Li, Ying-Ying Liu, Xiaohui Song, Zhuo-Yang Zhang, Liang-Qi Zhang, Shutao Zhao, Ke |
| contents | Efficient quantum state preparation remains a central challenge in first-principles quantum simulations of dynamics in quantum field theories, where the Hilbert space is intrinsically infinite-dimensional. Here, we introduce a large language model (LLM)-assisted framework for quantum-circuit design that systematically scales state-preparation circuits to large lattice volumes. Applied to a 1+1d XY spin chain, the LLM autonomously discovers a compact 4-parameter circuit that captures boundary-induced symmetry breaking with sub-percent energy deviation, enabling successful validation on the \texttt{Zuchongzhi} quantum processor. Guided by this insight, we extend the framework to 2+1d quantum field theories, where scalable variational ansätze have remained elusive. For a scalar field theory, the search yields a symmetry-preserving, 3-parameter shallow-depth ansatz whose optimized parameters converge to size-independent constants for lattices $n \ge 4$, providing, to our knowledge, the first scalable ansatz for this class of 2+1d models. Our results establish a practical route toward AI-assisted, human-guided discovery in quantum simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Quantum State Preparation via Large-Language-Model-Driven Discovery Cao, Qing-Hong Hou, Zong-Yue Li, Ying-Ying Liu, Xiaohui Song, Zhuo-Yang Zhang, Liang-Qi Zhang, Shutao Zhao, Ke Quantum Physics Artificial Intelligence High Energy Physics - Lattice High Energy Physics - Phenomenology Efficient quantum state preparation remains a central challenge in first-principles quantum simulations of dynamics in quantum field theories, where the Hilbert space is intrinsically infinite-dimensional. Here, we introduce a large language model (LLM)-assisted framework for quantum-circuit design that systematically scales state-preparation circuits to large lattice volumes. Applied to a 1+1d XY spin chain, the LLM autonomously discovers a compact 4-parameter circuit that captures boundary-induced symmetry breaking with sub-percent energy deviation, enabling successful validation on the \texttt{Zuchongzhi} quantum processor. Guided by this insight, we extend the framework to 2+1d quantum field theories, where scalable variational ansätze have remained elusive. For a scalar field theory, the search yields a symmetry-preserving, 3-parameter shallow-depth ansatz whose optimized parameters converge to size-independent constants for lattices $n \ge 4$, providing, to our knowledge, the first scalable ansatz for this class of 2+1d models. Our results establish a practical route toward AI-assisted, human-guided discovery in quantum simulation. |
| title | Scalable Quantum State Preparation via Large-Language-Model-Driven Discovery |
| topic | Quantum Physics Artificial Intelligence High Energy Physics - Lattice High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2505.06347 |