# Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization

arXiv: https://arxiv.org/abs/2607.00939 · HTML: https://mlsanity.com/paper/2607.00939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 24 of 24)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models](https://mlsanity.com/paper/2406.14712/) (2024)
- [QuanBench: Benchmarking Quantum Code Generation with Large Language Models](https://mlsanity.com/paper/2510.16779/) (2025)
- [Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction](https://mlsanity.com/paper/2504.14557/) (2025)
- [QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming](https://mlsanity.com/paper/2508.20134/) (2025)
- [QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL](https://mlsanity.com/paper/2510.00967/) (2025)
- [QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits](https://mlsanity.com/paper/2504.16350/) (2025)
- [QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges](https://mlsanity.com/paper/2506.20008/) (2025)
- [Quantum Software Engineering: Roadmap and Challenges Ahead](https://mlsanity.com/paper/2404.06825/) (2024)
- [Quantum Software Engineering: Landscapes and Horizons](https://mlsanity.com/paper/2007.07047/) (2020)
- [LLM-QUBO: An End-to-End Framework for Automated QUBO Transformation from Natural Language Problem Descriptions](https://mlsanity.com/paper/2509.00099/) (2025)
- [Quantum Artificial Intelligence for Software Engineering: the Road Ahead](https://mlsanity.com/paper/2505.04797/) (2025)
- [A Methodological Analysis of Empirical Studies in Quantum Software Testing](https://mlsanity.com/paper/2601.08367/) (2026)
- [QBugLM: An Agentic Benchmarking Framework for LLM-based Quantum Software Debugging](https://mlsanity.com/paper/2606.07314/) (2026)
- [Applying and Extending the Delta Debugging Algorithm for Elevator Dispatching Algorithms (Experience Paper)](https://mlsanity.com/paper/2305.17803/) (2023)
- [Test Case Minimization with Quantum Annealers](https://mlsanity.com/paper/2308.05505/) (2023)
- [Quantum Approximate Optimization Algorithm for Test Case Optimization](https://mlsanity.com/paper/2312.15547/) (2023)
- [Reformulating Regression Test Suite Optimization using Quantum Annealing -- an Empirical Study](https://mlsanity.com/paper/2411.15963/) (2024)
- [Quantum-Based Software Engineering](https://mlsanity.com/paper/2505.23674/) (2025)
- [Quantum Optimization for Software Engineering: A Survey](https://mlsanity.com/paper/2506.16878/) (2025)
- [Empirical Studies on Quantum Optimization for Software Engineering: A Systematic Analysis](https://mlsanity.com/paper/2510.27113/) (2025)
- [Ising-based Test Optimization and Benchmarking](https://mlsanity.com/paper/2604.10450/) (2026)
- [Q-READY: Predictive Feasibility Assessment for Hybrid Quantum-Classical Applications](https://mlsanity.com/paper/2606.16201/) (2026)

## Abstract (arXiv)

Quantum computing is increasingly explored for software engineering (SE) optimization, but translating natural-language (NL) task-level requirements into executable quantum applications still demands substantial quantum and programming expertise. We present QPipe, a large language model (LLM)-based multi-agent architecture that autonomously turns NL requirements into traceable quantum-application workflows through specialized agents for requirement parsing, formulation, code generation, review, execution, and verification. We evaluate QPipe on 20 NL requirements, each associated with a real-world benchmark and a test-optimization problem. QPipe successfully completes the key stages of quantum-application generation across requirements, achieving average rates of 100% for code compilation and 96.7% for application execution and final-result combination, with average generation costs of 260.1 seconds and 1.89M tokens per requirement. Among the generated quantum applications that execute successfully, the returned solutions outperform the offline genetic algorithm baseline in most cases. Ablation results further show that QPipe's advantage depends on retaining code-generation skills, task knowledge, review feedback, and multi-agent decomposition. These results indicate that agentic coordination can support generation of executable quantum applications for tackling test optimization problems from real-world benchmarks.