Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model
This study integrates a femtosecond laser-pumped Coherent Ising Machine (CIM) with an LLM-driven agentic system using LangGraph and LangChain frameworks. LLMs effectively perform QUBO/Ising model calibration, constraint weight iteration, and validation of literature-reported schemes. All tasks use domestic large models and CIM hardware, achieving practical quantum CIM empowerment fully based on domestic core technologies. A new paradigm is discovered where agent-assisted quantum computing iterations reciprocally enhance the agent's own problem-solving capability.
Article intelligence
Key points
- Integration of femtosecond laser-pumped CIM with LLM-driven agentic system
- LLMs perform QUBO/Ising calibration, constraint iteration, and validation
- Fully implemented with domestic large models and CIM hardware
- Reciprocal enhancement between agent and quantum computing iterations
Why it matters
This matters because integration of femtosecond laser-pumped CIM with LLM-driven agentic system.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.23934] Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model
[Submitted on 24 Apr 2026]
Title:Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model
View a PDF of the paper titled Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model, by Wang Rui and Lu Diannan
View PDF HTML (experimental)
Abstract: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.
Comments: 21 pages 7 figures
Subjects:
Artificial Intelligence (cs.AI); Quantum Physics (quant-ph)
MSC classes: 68Q12, 68T01, 90C27
ACM classes: I.2.6; I.2.10; F.2.1; F.2.2
Cite as: arXiv:2605.23934 [cs.AI]
(or arXiv:2605.23934v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.23934
arXiv-issued DOI via DataCite
Submission history
From: Rui Wang [view email] [v1] Fri, 24 Apr 2026 03:45:08 UTC (9,502 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Practical Quantum CIM Empowerment via All-Domestic-Core Agentic Large Model, by Wang Rui and Lu Diannan
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-05
Change to browse by:
cs quant-ph
References & Citations
INSPIRE HEP
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)