AI 中文总结
研究混合量子-经典工作流性能评估框架缺失问题，引入运行时模型分解工作流执行成本，通过通信与计算比率及可行性约束量化性能，应用于代表性工作流表明其对计算密集型应用提升有限，对实时任务至关重要，还能随硬件发展识别交叉条件及设定应用层性能。
AI 中文摘要
混合量子-经典工作流有望支撑实际量子计算应用，但量子和高性能计算社区缺乏一个共享框架来判断其集成需求在何处及何时最为关键。为此，我们引入一个运行时模型，将工作流执行分解为量子计算、经典计算和通信成本。在应用层，此分解的通信与计算比率量化工作流是受通信限制还是受计算限制；在实时层，可行性约束决定是否能满足定时要求，反应时间则设定容错计算的逻辑时钟速度。将该模型应用于代表性工作流表明，如今量子处理器与高性能计算基础设施的共置对计算密集型应用的性能提升可忽略不计，而紧密集成对大规模量子计算所需的实时任务（如量子纠错）仍至关重要。不过，我们讨论了即使在应用层，这些评估也可能随硬件发展而变化，展示了该模型如何识别特定的交叉条件，以及在容错情况下反应时间如何设定应用层性能。
英文摘要
Hybrid quantum-classical workflows are expected to underpin practical quantum computing applications, yet the quantum and HPC communities lack a shared framework for reasoning about where and when their integration requirements matter most. Such a framework must separate two distinct levels of analysis: the application level, where communication overhead affects runtime performance, and the real-time level, where it determines feasibility. To address this, we introduce a runtime model that decomposes workflow execution into quantum compute, classical compute, and communication costs. At the application level, a communication-to-computation ratio from this decomposition quantifies whether a workflow is communication-bound or compute-bound; at the real-time level, a feasibility constraint determines whether timing requirements can be met at all, with the reaction time setting the logical clock speed of fault-tolerant computation once they are. Application of this model to representative workflows demonstrates that co-location of quantum processors with HPC infrastructure offers negligible performance benefit for compute-intensive applications today, while tight integration remains crucial for real-time tasks such as quantum error correction needed for large scale quantum computations. However, we discuss how even at the application level these assessments may shift with hardware evolution, illustrating how the model can identify specific crossover conditions, and how, under fault tolerance, the reaction time can set application-level performance.
