标题： QSpark：面向可靠Qiskit代码生成 显示英文标题

标题： QSpark: Towards Reliable Qiskit Code Generation

摘要： 量子电路必须具有容错性，但像Granite-20B-Code和StarCoder这样的LLM经常输出有缺陷的Qiskit代码。我们使用一个丰富标注的合成数据集，对一个32 B模型进行了两种强化学习方法的微调，即组相对策略优化（GRPO）和几率比偏好优化（ORPO）。在Qiskit HumanEval基准测试中，ORPO达到56.29% Pass@1（$\approx+10$ pp超过Granite-8B-QK），GRPO达到49%，两者都超过了所有通用基线；在原始HumanEval中，它们的得分分别为65.90%和63.00%。GRPO在基础任务上表现优异（42/54），ORPO在中级任务上表现优异（41/68），而两者都无法解决五个高级任务，这表明在AI辅助的量子编程中取得了明显进展，但仍有许多改进空间。 显示英文摘要

摘要： Quantum circuits must be error-resilient, yet LLMs like Granite-20B-Code and StarCoder often output flawed Qiskit code. We fine-tuned a 32 B model with two RL methods, Group Relative Policy Optimization (GRPO) and Odds-Ratio Preference Optimization (ORPO), using a richly annotated synthetic dataset. On the Qiskit HumanEval benchmark, ORPO reaches 56.29\% Pass@1 ($\approx+10$ pp over Granite-8B-QK) and GRPO hits 49\%, both beating all general-purpose baselines; on the original HumanEval they score 65.90\% and 63.00\%. GRPO excels on basic tasks (42/54), ORPO on intermediate ones (41/68), and neither solves the five advanced tasks, highlighting clear gains yet room for progress in AI-assisted quantum programming.