发表机构
Indian Institute of Technology Delhi; IBM Quantum, IBM Research（德里印度理工学院; IBM量子，IBM研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SQD-Agent是一个基于LLM的智能体框架，将自然语言意图转化为量子化学工作流，自动化SQD算法流程，降低专业门槛，支持模块化集成和错误缓解分析。
AI 中文摘要
量子算法和量子硬件正朝着科学应用的有前景范式迈进。然而，将特定领域的问题转化为可执行的混合量子-经典工作流，对于应用研究人员而言仍是一个重大障碍，因为这需要量子算法方面的专业知识、量子编程的细微差别以及硬件感知的系统集成。与此同时，人工智能，尤其是基于LLM的智能体，越来越能够解释自然语言意图、推理复杂工作流，并将高层目标转化为可执行代码和构建计算管道。在这项工作中，我们引入了SQD Agent，一个基于LLM的智能体框架，它将自然语言用户意图转化为量子化学应用中的可执行工作流，其中使用了基于样本的量子对角化（SQD）算法家族。通过自动化这种转化，SQD Agent降低了所需的人类专业知识和配置开销，从而简化了对于刚接触量子领域的应用研究人员在混合量子-经典环境中的实验。SQD Agent采用模块化和可扩展的架构，支持异构量子后端、经典求解器和工作流组件的无缝集成，确保了对快速演变的量子生态系统的适应性。该框架进一步包含了交互式功能，用于按需性能分析、瓶颈分析、资源优化、智能结果缓存和收敛性可视化。关键特性包括量子化学实验、在真实量子硬件上的错误缓解，以及根据其潜在的错误恢复行为和计算预算对候选缓解方案进行分析，帮助用户理解其实际权衡，并决定在后续实验中探索哪些策略。
英文摘要
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.
CommentsAccepted in IEEE International Conference on High Performance Computing (HiPC 2026)
