arXiv cs.MA· Kislaya Tiwari, Anupama Ray·· 3 天前AI 评分22
SQD-Agent：LLM 驱动的量子化学工作流智能体框架
SQD-Agent: LLM-driven agentic framework for Quantum Chemistry workflows
AI 导读
SQD-Agent 是一个基于 LLM 的智能体框架，可将自然语言意图转化为基于 Sample-Based Quantum Diagonalization（SQD）算法的量子化学可执行工作流，降低应用研究者使用混合量子-经典流程的专业门槛。
正文 · 原文
Abstract:Quantum algorithms and quantum hardware are advancing towards a promising paradigm for scientific applications. However, translating domain-specific problems into executable hybrid quantum-classical workflows remains a significant barrier for application researchers due to the required expertise in quantum algorithms, nuances in quantum programming, and hardware-aware system integration. At the same time, AI and primarily LLM based agents are increasingly capable of interpreting natural-language intent, reasoning over complex workflows, and translating high-level objectives into executable code and building computational pipelines. In this work, we introduce SQD Agent, an LLM-based agentic framework that translates natural-language user intent into executable workflows for Quantum Chemistry applications where algorithms from the Sample-Based Quantum Diagonalization (SQD) family are used. By automating this translation, SQD Agent reduces the level of human expertise and configuration overhead required, thereby simplifying experimentation in hybrid quantum-classical settings for application researchers new to quantum. SQD Agent adopts a modular and extensible architecture that supports seamless integration of heterogeneous quantum backends, classical solvers, and workflow components, ensuring adaptability to rapidly evolving quantum ecosystems. The framework further incorporates interactive capabilities for on-demand profiling, bottleneck analysis, resource optimization, intelligent result caching, and convergence visualization. Key features include quantum chemistry experiments, error mitigation on real quantum hardware, together with analysis of candidate mitigation schemes in terms of their potential error-recovery behavior and computational budget, helping users understand their practical trade-offs and decide which strategies to explore in subsequent experiments.
| Comments: | Accepted in IEEE International Conference on High Performance Computing (HiPC 2026) |
| Subjects: | Quantum Physics (quant-ph); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2609.39302 [quant-ph] |
| (or arXiv:2609.39302v2 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2609.39302 arXiv-issued DOI via DataCite |
Submission history
From: Anupama Ray Dr. [view email]
[v1]
Wed, 30 Sep 2026 08:45:57 UTC (5,965 KB)
[v2]
Wed, 14 Oct 2026 12:31:16 UTC (5,967 KB)
来源：arXiv cs.MA · arxiv.org