Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model
For researchers and practitioners in quantum computing, this work reduces the barrier to using CIMs by automating complex modeling tasks, though it is an incremental integration of existing technologies.
This work integrates a Coherent Ising Machine with an LLM-driven agentic system to automate QUBO/Ising model calibration and constraint weight iteration, achieving practical quantum CIM empowerment using all-domestic hardware and models. It also discovers a reciprocal enhancement loop where agent-assisted quantum computing iterations improve the agent's problem-solving capability.
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.