SQD-Agent: LLM-driven agentic framework for Quantum Chemistry workflows
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.
Related
- Aitomia: An Agentic Framework for AI-Driven Atomistic and Quantum Chemical Simulations
- Lowering the implementation barrier of neutral-atom quantum computing with agentic workflows
- LatticeMind: A Validation-Gated Agentic Framework for Density Functional Theory Workflows
- The Python simulations of chemistry framework: 10 years of an open-source quantum chemistry project
- QBugLM: An Agentic Benchmarking Framework for LLM-based Quantum Software Debugging
BibTeX
@misc{tiwari2026sqdagent,
title = {{SQD-Agent: LLM-driven agentic framework for Quantum Chemistry workflows}},
author = {Kislaya Tiwari and Anupama Ray},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.39302},
eprint = {2609.39302},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.39302},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - SQD-Agent: LLM-driven agentic framework for Quantum Chemistry workflows
AU - Kislaya Tiwari
AU - Anupama Ray
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2609.39302
UR - https://arxiv.org/abs/2609.39302
AB - 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.
ER -