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

TL;DR

This paper introduces a comprehensive framework for evaluating hybrid quantum programs as complete workflows, including new metrics, a Python toolkit, and practical guidance for assessing quantum readiness and utility.

Contribution

It formalizes a workflow-aware QRL score, defines a normalized speedup metric, and provides Python implementations for practical evaluation of hybrid quantum workflows.

Findings

Introduces a workflow-aware Quantum Readiness Level score

Defines a normalized speedup metric under quality constraints

Provides Python reference implementations for evaluation

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.