发表机构
University of Illinois Urbana-Champaign; IBM Research（伊利诺伊大学厄巴纳-香槟分校; IBM研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出LLQM元框架，通过持续剖析量子-经典流水线的细粒度任务和资源依赖，揭示跨阶段交互，并在IBM Heron r2上验证其能暴露瓶颈并支持硬件、保真度及工作负载感知的优化。
AI 中文摘要
量子应用日益以多阶段量子-经典流水线的形式执行，将QPU计算与电路生成、转译、布局映射、量子误差缓解（QEM）及后处理等经典阶段交错进行。这些阶段具有多样化的资源需求，并在漂移的硬件噪声下表现出随机行为，然而现有工作流框架将它们视为静态、孤立的组件。我们提出LLQM（底层量子机器），一个面向量子-经典流水线的剖析驱动元框架。LLQM将流水线分解为细粒度任务，并持续剖析其CPU/GPU、内存、QPU及队列依赖关系，同时结合实时硬件状态。这种统一的运行时抽象捕获了跨阶段的资源依赖，揭示了经典与量子决策如何相互作用，从而能够表征它们对保真度和资源消耗的影响。我们使用QEM作为代表性流水线阶段，在IBM 156量子比特Heron r2处理器上，对多达100量子比特和1e7个转译门的电路进行了评估。结果表明，持续剖析能够暴露运行时瓶颈，并实现硬件、保真度和工作负载感知的优化。
英文摘要
Quantum applications increasingly execute as multi-stage quantum-classical pipelines, interleaving QPU computation with classical stages like circuit generation, transpilation, layout mapping, quantum error mitigation (QEM), and post-processing. These stages have diverse resource requirements and exhibit stochastic behavior under drifting hardware noises, yet existing workflow frameworks treat them as static, isolated components. We present LLQM (Low-Level Quantum Machine), a profiling-driven meta-framework for quantum-classical pipelines. LLQM decomposes pipelines into fine-grained tasks and continuously profiles their CPU/GPU, memory, QPU, and queue dependencies alongside real-time hardware states. This unified runtime abstraction captures cross-stage resource dependencies and reveals how classical and quantum decisions interact, enabling characterization of their impact on fidelity and resource consumption. We evaluate LLQM using QEM as a representative pipeline stage, on IBM 156-qubit Heron r2 processors with circuits up to 100 qubits and 1e7 transpiled gates. Our results show that continuous profiling exposes runtime bottlenecks and enables hardware-, fidelity-, and workload-aware optimizations.
