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.
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BibTeX
@misc{bansal2026towards,
title = {{Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines}},
author = {Ayush Bansal and Owen Cochell and Santiago Núñez-Corrales and Marcos Frenkel and Seetharami Seelam and Apoorve Mohan and Tianyin Xu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.05814},
eprint = {2609.05814},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.05814},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Towards Continuous Profiling and Optimization of Quantum-Classical Pipelines
AU - Ayush Bansal
AU - Owen Cochell
AU - Santiago Núñez-Corrales
AU - Marcos Frenkel
AU - Seetharami Seelam
AU - Apoorve Mohan
AU - Tianyin Xu
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.05814
UR - https://arxiv.org/abs/2609.05814
AB - 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.
ER -