Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines

• URL: http://arxiv.org/abs/2609.05814v2
• Date: Thu, 10 Sep 2026 15:01:31 GMT
• Title: Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines
• Abstract summary: We present LLQM (Low-Level Quantum Machine), a profiling-driven meta-framework for quantum-classical pipelines.<n>We show that continuous profiling exposes bottlenecks and enables hardware-, fidelity-, and workload-aware runtime optimizations.
• Score: 3.3978978857739204
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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