# QBugLM: An Agentic Benchmarking Framework for LLM-based Quantum Software Debugging

arXiv: https://arxiv.org/abs/2606.07314 · HTML: https://mlsanity.com/paper/2606.07314/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 5 Jun 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, 21 of 21)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [SPoC: Search-based Pseudocode to Code](https://mlsanity.com/paper/1906.04908/) (2019)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models](https://mlsanity.com/paper/2406.14712/) (2024)
- [Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code](https://mlsanity.com/paper/2405.19495/) (2024)
- [Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization](https://mlsanity.com/paper/2504.11109/) (2025)
- [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)
- [QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges](https://mlsanity.com/paper/2506.20008/) (2025)
- [PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation](https://mlsanity.com/paper/2507.19562/) (2025)
- [Testing and Debugging Quantum Programs: The Road to 2030](https://mlsanity.com/paper/2405.09178/) (2024)
- [On Repairing Quantum Programs Using ChatGPT](https://mlsanity.com/paper/2401.14913/) (2024)
- [Bugs in Quantum Computing Platforms: An Empirical Study](https://mlsanity.com/paper/2110.14560/) (2021)
- [QBugs: A Collection of Reproducible Bugs in Quantum Algorithms and a Supporting Infrastructure to Enable Controlled Quantum Software Testing and Debugging Experiments](https://mlsanity.com/paper/2103.16968/) (2021)
- [QuSBT: Search-Based Testing of Quantum Programs](https://mlsanity.com/paper/2204.08561/) (2022)
- [QChecker: Detecting Bugs in Quantum Programs via Static Analysis](https://mlsanity.com/paper/2304.04387/) (2023)
- [Leveraging Mutation Analysis for LLM-based Repair of Quantum Programs](https://mlsanity.com/paper/2601.12273/) (2026)
- [Asking the Right Questions: Improving Reasoning with Generated Stepping Stones](https://mlsanity.com/paper/2602.19069/) (2026)

## Built on by (most cited first, 2 of 2)

- [Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization](https://mlsanity.com/paper/2607.00939/) (2026)
- [QuSema: Detecting Silent Bugs in Quantum Libraries via Quantum-knowledge-enhanced Agents](https://mlsanity.com/paper/2610.10258/) (2026)

## Abstract (arXiv)

Quantum software bugs often yield silent, incorrect outputs rather than explicit errors, making them particularly difficult to detect and repair with conventional techniques. Although large language models (LLMs) have shown strong performance on classical software engineering tasks, their ability to debug quantum code remains largely unexplored. To bridge this gap, we propose QBugLM, a multi-agent framework that automates the quantum software debugging pipeline, from taxonomy-driven bug injection to LLM-based detection and repair, and finally to simulation-based validation, for framework-agnostic OpenQASM 3.0 programs. We further conduct a comprehensive case study using QBugLM to benchmark two LLMs, Claude 4.6 Sonnet and Qwen3 Coder Next, across different prompting strategies, bug categories, and quantum programs. Our results show that iterative feedback is critical, as a single retry raises Pass@1 from below 25% to above 80%. Moreover, simpler structured prompting can even outperform Chain-of-Thought and ReAct for reasoning-capable models under fixed-resource constraints. Our work takes initial steps toward benchmarking LLM capabilities for debugging quantum programs and offers practical insights to support future efforts in automated quantum software repair.