Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines

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.

Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Operating Systems (cs.OS)

arXiv:2609.05814 [quant-ph]

(or arXiv:2609.05814v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.05814 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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