AI 中文总结
本研究提出受MLflow启发的Qiskit自动日志记录框架，扩展QProv模型并结合MLflow Tracking Server，提升量子编译器可观测性，支持其工作流的可复现评估。
AI 中文摘要
要理解量子编译技术的有效性，需对整个 transpilation（转译）过程而非仅最终电路指标进行观测。本演示提出一种受MLflow启发的Qiskit自动日志记录框架，可自动捕获编译器溯源信息，包括转译阶段、 pass（ passes，编译过程中的处理步骤）级执行数据、后端特性、编译器配置及执行结果。该框架扩展了QProv溯源模型，加入编译器专属信息，并将收集的数据存储于MLflow Tracking Server以用于分析和可视化。该方法消除了手动插装，提升了编译器可观测性，支持量子编译工作流的可复现评估。
英文摘要
Understanding the effectiveness of quantum compilation techniques requires visibility into the entire transpilation process, not just the final circuit metrics. This demonstration presents an MLflow-inspired autologging framework for Qiskit that automatically captures compiler provenance, including transpilation stages, pass-level execution data, backend characteristics, compiler configuration, and execution results. The framework extends the QProv provenance model with compiler-specific information and stores the collected data in an MLflow Tracking Server for analysis and visualization. By eliminating manual instrumentation, the proposed approach improves compiler observability and supports reproducible evaluation of quantum compilation workflows.
