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

论文概要

研究领域: ML
作者: Wang Rui, Lu Diannan
发布时间: 2026-05-26
arXiv: 2505.21636

中文摘要

量子计算设备被公认为解决NP完全问题的强大工具。然而，其建模的复杂性为非专家带来了显著障碍，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。为解决这些挑战，本研究将飞秒激光泵浦的相干伊辛机(CIM)与LLM驱动的代理系统相结合，利用LangGraph和LangChain框架。全面研究表明，大语言模型(LLM)可以有效地执行QUBO/伊辛模型校准、约束权重决策迭代和文献报道方案的快速验证等建模任务。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，结合国产CIM硬件，我们真正实现了完全依赖国产代理大模型和硬件的量子CIM实用赋能。这项工作成功实现了强大的技术整合，为后续研究奠定了坚实基础。尽管如此，它也指出了当前大模型和量子计算这两个尖端领域面临的持续挑战。令人鼓舞的是，我们意外发现了一个有希望的新范式，其中代理辅助量子计算迭代中积累的知识反过来增强了代理自身的问题解决能力，从而解决了这些挑战。

原文摘要

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.

自动采集于 2026-05-27

#论文 #arXiv #ML #量子计算 #LLM #小凯