实用量子CIM赋能：基于全自主核心智能体大模型
Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model
AI总结：
本研究将飞秒激光泵浦的相干伊辛机与LLM驱动的智能体系统结合，实现QUBO/Ising模型校准、约束权重决策迭代和文献方案快速验证，并完全基于国产大模型和硬件完成，同时发现智能体辅助量子计算迭代可反向增强智能体问题解决能力的新范式。
摘要：
量子计算设备被认为是解决NP完全问题的强大工具。然而，其建模的复杂性给非专业人士带来了显著障碍，而约束权重和建模方法的繁琐迭代也消耗了专家的大量精力。为应对这些挑战，本研究通过利用LangGraph和LangChain框架，将飞秒激光泵浦的相干伊辛机（CIM）与LLM驱动的智能体系统集成。综合研究表明，大语言模型（LLMs）可以有效执行建模任务，如QUBO/Ising模型校准、约束权重决策迭代以及文献报道方案的快速验证。值得注意的是，所有这些任务都可以完全基于国产大模型实现，结合国内开发的CIM硬件，我们真正实现了完全依赖全自主智能体大模型和硬件的实用量子CIM赋能。这项工作成功实现了稳健的技术集成，为后续研究奠定了坚实基础。然而，它也指出了当前阶段大模型和量子计算这两个前沿领域持续存在的挑战。令人鼓舞的是，我们意外发现了一种有前景的新范式，其中智能体辅助的量子计算迭代积累的知识反向增强了智能体自身的问题解决能力，从而应对这些挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
