# QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges

arXiv: https://arxiv.org/abs/2506.20008 · HTML: https://mlsanity.com/paper/2506.20008/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges](https://mlsanity.com/paper/2412.03220/) (2024)
- [Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models](https://mlsanity.com/paper/2406.14712/) (2024)
- [A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead](https://mlsanity.com/paper/2310.10315/) (2023)
- [Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code](https://mlsanity.com/paper/2405.19495/) (2024)
- [COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis](https://mlsanity.com/paper/2408.05006/) (2024)
- [RGD: Multi-LLM Based Agent Debugger via Refinement and Generation Guidance](https://mlsanity.com/paper/2410.01242/) (2024)
- [A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG](https://mlsanity.com/paper/2503.02497/) (2025)

## Built on by (most cited first, 8 of 8)

- [PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation](https://mlsanity.com/paper/2507.19562/) (2025)
- [Quantum Verifiable Rewards for Post-Training Qiskit Code Assistant](https://mlsanity.com/paper/2508.20907/) (2025)
- [QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation](https://mlsanity.com/paper/2604.08570/) (2026)
- [Qiskit QuantumKatas: Adapting Microsoft's Quantum Computing exercises for LLM evaluation](https://mlsanity.com/paper/2605.27210/) (2026)
- [QBugLM: An Agentic Benchmarking Framework for LLM-based Quantum Software Debugging](https://mlsanity.com/paper/2606.07314/) (2026)
- [PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation](https://mlsanity.com/paper/2605.25572/) (2026)
- [Leveraging LLM-Based Agentic Systems to Generate Quantum Applications for Test Optimization](https://mlsanity.com/paper/2607.00939/) (2026)
- [QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code](https://mlsanity.com/paper/2610.13811/) (2026)

## Abstract (arXiv)

Recent advances in Large Language Models (LLMs) have demonstrated strong potential in code generation, yet their effectiveness in quantum computing remains underexplored. This paper benchmarks LLMs for PennyLane-based quantum code generation using real-world challenges from the Quantum Hackathon (QHack). We introduce QHackBench, a novel benchmark dataset derived from QHack competitions, and evaluate model performance under vanilla prompting and Retrieval-Augmented Generation (RAG). Our structured evaluation framework assesses functional correctness, syntactic validity, and execution success across varying challenge difficulties. Results indicate that RAG-enhanced models, supplemented with an augmented PennyLane dataset, approximately generate similar results as the standard prompting, particularly in complex quantum algorithms. Additionally, we introduce a multi-agent evaluation pipeline that iteratively refines incorrect solutions, further enhancing execution success rates. To foster further research, we commit to publicly releasing QHackBench, along with our evaluation framework and experimental results, enabling continued advancements in AI-assisted quantum programming.