LLM-driven framework simplifies quantum chemistry workflows for researchers
SQD-Agent: LLM-driven agentic framework for Quantum Chemistry workflows
Multiagent Systems
Summary
Quantum computing promises to solve complex scientific problems but is hard to use because it requires special knowledge. The authors created SQD Agent, a tool that helps translate simple language instructions into working quantum chemistry experiments. This tool lowers the expertise needed and helps users run and improve hybrid quantum-classical calculations more easily. It also assists with analyzing errors, optimizing resources, and understanding results on real quantum hardware.
What this means in practice
- •For quantum software developers: Create automated pipelines that convert natural language instructions into executable quantum chemistry workflows using multiple quantum backends and classical solvers.
- •For computational chemists: Run hybrid quantum-classical experiments with built-in error mitigation and result analysis to explore practical trade-offs on real quantum hardware more efficiently.
Authors
Kislaya Tiwari, Anupama Ray
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.