Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model

Computer Science > Artificial Intelligence [Submitted on 24 Apr 2026] Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model Wang Rui, Lu Diannan Quantum computing devices are recognized as powerful tools for solving NP-complete problems. However, the intricacy of their modeling presents notable barriers for non-specialists, while the tedious iteration of constraint weights and modeling methodologies also consumes substantial effort on the part of experts. To address these challenges, this study integrates a femtosecond laser-pumped Coherent Ising Machine (CIM) with an LLM-driven agentic system by leveraging the LangGraph and LangChain frameworks. Comprehensive investigations demonstrate that large language models (LLMs) can effectively perform such tasks in modeling as QUBO/Ising model calibration, constraint weight decision iteration and rapid validation of literature-reported schemes. Notably, all these tasks can be fully implemented based on domestic large models, combined with domestically developed CIM hardware, we truly achieve the practical empowerment of quantum CIM that fully relies on all-domestic agentic large models and hardware. This work successfully realizes robust technological integration, laying a solid foundation for subsequent research. Nevertheless, it also identifies the persisting challenges in the two cutting-edge fields of large models and quantum computing at the current stage. Encouragingly, we unexpectedly discover a promising new paradigm where accumulated knowledge from agent-assisted quantum computing iterations reciprocally enhances the agent's own problem-solving capability, thereby addressing these challenges. Comments: 21 pages 7 figures Subjects: Artificial Intelligence (cs.AI); Quantum Physics (quant-ph) MSC classes: 68Q12, 68T01, 90C27 ACM classes: I.2.6; I.2.10; F.2.1; F.2.2 Cite as: arXiv:2605.23934 [cs.AI]   (or arXiv:2605.23934v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.23934 Focus to learn more Submission history From: Rui Wang [view email] [v1] Fri, 24 Apr 2026 03:45:08 UTC (9,502 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs quant-ph References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)