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HQPEF-Py: Metrics, Python Patterns, and Guidance for Evaluating Hybrid Quantum Programs

Michael Adjei Osei, Sidney Shapiro

TL;DR

HQPEF-Py addresses end-to-end evaluation of hybrid quantum programs by formalizing a workflow-aware scoring system, $QRL$, and a solver-agnostic measure of utility, $S_{norm}(tau)$, under matched budgets $B$. It instruments per-stage timing and drift to enable bottleneck attribution and provides Python reference patterns for reproducible implementation. The approach emphasizes governance, reproducibility, and fair comparisons across classical and quantum backends, with a synthetic demonstration validating the benchmarking harness. Together, these contributions offer a practical, auditable pathway to assess maturity and potential quantum advantage in real-world hybrid 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.

HQPEF-Py: Metrics, Python Patterns, and Guidance for Evaluating Hybrid Quantum Programs

TL;DR

HQPEF-Py addresses end-to-end evaluation of hybrid quantum programs by formalizing a workflow-aware scoring system, , and a solver-agnostic measure of utility, , under matched budgets . It instruments per-stage timing and drift to enable bottleneck attribution and provides Python reference patterns for reproducible implementation. The approach emphasizes governance, reproducibility, and fair comparisons across classical and quantum backends, with a synthetic demonstration validating the benchmarking harness. Together, these contributions offer a practical, auditable pathway to assess maturity and potential quantum advantage in real-world hybrid 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.

Paper Structure

This paper contains 27 sections, 10 equations, 1 figure.

Table of Contents

  1. Introduction
  2. Workflow Readiness: Formalization and Scoring
  3. Example (QRL scoring).
  4. Utility of Quantumness (UQ) Under Matched Budgets
  5. Example (computing $S_{\text{norm}}$).
  6. Methodological Context and Guidance
  7. Choosing $Q(\cdot)$ and $\tau$.
  8. Budgets and fairness.
  9. Instance design.
  10. Uncertainty and reporting.
  11. Audit usage.
  12. Governance.
  13. Workflow Audit and Bottleneck Attribution
  14. Example (stage-share audit).
  15. Reference Python Patterns
  16. ...and 12 more sections

Figures (1)

  • Figure 1: Conceptual instrumentation of a hybrid pipeline. Hooks capture per-stage timings and calibration drift, while the benchmarking harness enforces resource matching and strong classical baselines.