用国产大模型和量子硬件实现量子计算实用化。
Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model
- 用国产大模型驱动智能代理自动调参建模。
- 实现全链路国产化量子计算系统，支持快速验证文献方案。
- 发现智能体通过迭代反哺能力提升，形成良性循环。
量子计算设备被视为求解NP完全问题的强大工具。然而，其建模复杂性对非专业人员构成显著障碍，而约束权重与建模方法的反复迭代也耗费专家大量精力。为此，本研究将飞秒激光泵浦的相干伊辛机（CIM）与基于LangGraph和LangChain框架的LLM驱动智能体系统相结合。全面研究表明，大语言模型（LLMs）可有效完成诸如QUBO/伊辛模型校准、约束权重迭代决策及文献报告方案的快速验证等任务。值得注意的是，所有这些任务均可基于国产大模型实现；结合国产自研的CIM硬件，真正实现了完全依赖国内大模型与硬件的量子CIM实用化赋能。该工作成功实现了关键技术的深度融合，为后续研究奠定了坚实基础。然而，也揭示了当前大模型与量子计算两大前沿领域仍存在的挑战。令人鼓舞的是，我们意外发现一种新范式：通过智能体辅助量子计算迭代积累的知识，可反向增强智能体自身的问题求解能力，从而缓解上述挑战。
原文摘要 · Abstract (English)
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.
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