🤖 AI Summary
Existing hybrid quantum programs lack end-to-end, workflow-level evaluation methodologies.
Method: This paper proposes a workflow-aware systematic assessment framework: (i) it formally defines workflow-oriented quantum readiness levels and quantum utility metrics; (ii) it introduces a quality-constrained normalized speedup ratio, a workflow-aware quantum readiness score, and a time–drift joint auditing mechanism for hybrid pipelines; and (iii) it establishes an evaluation system balancing budget alignment and experimental reproducibility. A Python-based open-source reference tool is implemented, integrating Qiskit, PennyLane, and other frameworks to support metric instantiation and automated auditing of classical–quantum co-solvers.
Contribution/Results: The work delivers a reusable metrics library and standardized auditing patterns, significantly improving the accuracy of performance evaluation and enabling precise bottleneck identification in hybrid quantum workflows.
📝 Abstract
We study how to evaluate hybrid quantum programs as end-to-end workflows rather than as isolated devices or algorithms. Building on the Hybrid Quantum Program Evaluation Framework (HQPEF), we formalize a workflow-aware Quantum Readiness Level (QRL) score; define a normalized speedup under quality constraints for the Utility of Quantumness (UQ); and provide a timing-and-drift audit for hybrid pipelines. We complement these definitions with concise Python reference implementations that illustrate how to instantiate the metrics and audit procedures with state-of-the-art classical and quantum solvers (e.g., via Qiskit or PennyLane), while preserving matched-budget discipline and reproducibility.