QCoder Benchmark: Bridging Language Generation and Quantum Hardware through Simulator-Based Feedback
This work addresses a gap in LLM evaluation for hardware-interactive domains like quantum programming, though it is incremental as it focuses on benchmarking rather than new methods.
The paper tackles the problem of evaluating large language models (LLMs) on quantum programming tasks by introducing QCoder Benchmark, a framework that uses simulator-based feedback and human-written code from contests, resulting in GPT-4o achieving only 18.97% accuracy while reasoning-based models like o3 reach up to 78% accuracy, outperforming human-written codes at 39.98%.
Large language models (LLMs) have increasingly been applied to automatic programming code generation. This task can be viewed as a language generation task that bridges natural language, human knowledge, and programming logic. However, it remains underexplored in domains that require interaction with hardware devices, such as quantum programming, where human coders write Python code that is executed on a quantum computer. To address this gap, we introduce QCoder Benchmark, an evaluation framework that assesses LLMs on quantum programming with feedback from simulated hardware devices. Our benchmark offers two key features. First, it supports evaluation using a quantum simulator environment beyond conventional Python execution, allowing feedback of domain-specific metrics such as circuit depth, execution time, and error classification, which can be used to guide better generation. Second, it incorporates human-written code submissions collected from real programming contests, enabling both quantitative comparisons and qualitative analyses of LLM outputs against human-written codes. Our experiments reveal that even advanced models like GPT-4o achieve only around 18.97% accuracy, highlighting the difficulty of the benchmark. In contrast, reasoning-based models such as o3 reach up to 78% accuracy, outperforming averaged success rates of human-written codes (39.98%). We release the QCoder Benchmark dataset and public evaluation API to support further research. (Codes and datasets are available at https://qcoder-bench.github.io/ )