HyQDB：混合量子工作流的LLM辅助调试

中文摘要 AI 辅助

混合量子程序的故障经常静默发生，然而现有的调试工具在检测和修复这些故障方面提供的支持有限。这些故障在领域专家报告的故障中占主导地位，但现有工具在公开数据上的评估却未能充分代表这种故障模式。我们的关键洞察是，故障分为两类，需要不同的策略：机械故障，可以进行确定性分析；概念故障，需要重建程序的意图。为了应对这一挑战，我们提出了HyQDB，一个分层代理，它将确定性的硬件、物理和优化证据注入到LLM修复过程中。当未检测到证据时，代理将此静默视为升级到第二层意图重建的信号，该层推断程序的行为并将其与实现进行调和。为了评估HyQDB，我们引入了QFaultBench，一个基于专家推导的故障分类法构建的基准，代表了混合程序真实故障模式。在一组保留的人类编写程序上，HyQDB将标准LLM的修复成功率从45%提高到75%。我们表明升级门控至关重要，使机械故障修复准确率提高了20%。

英文摘要

Hybrid quantum program failures frequently occur silently, yet existing debugging tools provide limited support for detecting and repairing them. These faults dominate the failures reported by domain experts, yet existing tools evaluate on public data that under-represents this failure mode. Our key insight is that faults divide into two classes that require different strategies: mechanical faults, which allow deterministic analysis, and conceptual faults, which require reconstructing the program's intent. To address this challenge, we present HyQDB, a tiered agent that injects deterministic hardware, physics and optimization evidence into the LLM repair process. When no evidence is detected, the agent treats this silence as a signal to escalate to a second intent-reconstruction tier, that infers the program's behaviour and reconciles it with the implementation. To evaluate HyQDB, we introduce QFaultBench, a benchmark built from an expert-derived fault taxonomy that represents the true failure modes of hybrid programs. On a held-out set of human-authored programs, HyQDB raises repair success over a standard LLM from 45% to 75%. We show that the escalation gate is crucial, giving a 20% improvement in mechanical fault repair accuracy.

发表机构

• Imperial College London（帝国理工学院）

机构由 AI 辅助整理，请以论文原文为准。