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

arXiv: https://arxiv.org/abs/2605.23934 · HTML: https://mlsanity.com/paper/2605.23934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Barren Plateaus in Variational Quantum Computing](https://mlsanity.com/paper/2405.00781/) (2024)
- [A Survey of Table Reasoning with Large Language Models](https://mlsanity.com/paper/2402.08259/) (2024)
- [Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization](https://mlsanity.com/paper/2504.11109/) (2025)
- [Scalable Quantum State Preparation via Large-Language-Model-Driven Discovery](https://mlsanity.com/paper/2505.06347/) (2025)
- [Solving Subset Sum Problems using Quantum Inspired Optimization Algorithms with Applications in Auditing and Financial Data Analysis](https://mlsanity.com/paper/2211.02653/) (2022)
- [Quantum-Boosted High-Fidelity Deep Learning](https://mlsanity.com/paper/2508.11190/) (2025)
- [Symbolic Analysis of Grover Search Algorithm via Chain-of-Thought Reasoning and Quantum-Native Tokenization](https://mlsanity.com/paper/2505.04880/) (2025)
- [Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges](https://mlsanity.com/paper/2601.16816/) (2026)
- [Mathematical Mechanism on Dynamical System Algorithms of the Ising Model](https://mlsanity.com/paper/2012.01156/) (2020)
- [Molecular Docking via Weighted Subgraph Isomorphism on Quantum Annealers](https://mlsanity.com/paper/2405.06657/) (2024)
- [MIMO with 1-bit Pre/Post-Coding Resolution: A Quantum Annealing Approach](https://mlsanity.com/paper/2406.04708/) (2024)

## Built on by (most cited first, 1 of 1)

- [Comprehensive pKa Data Augmentation from Limited Real Data through an Engineered Models-Quantum Framework](https://mlsanity.com/paper/2606.17077/) (2026)

## Abstract (arXiv)

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.