Hqpef-py Defines Quantum Readiness Level and Utility of Quantumness with Reproducible Python Metrics

Evaluating the performance of programs that combine classical and quantum computing remains a significant challenge, yet is crucial for realising the potential of this emerging technology. Michael Adjei Osei and Sidney Shapiro, both from the University of Lethbridge, address this need by developing a comprehensive system for assessing hybrid quantum programs as complete workflows, rather than isolated components. Their work formalises a new Quantum Readiness Level score, alongside a method for quantifying speedup while accounting for the inherent limitations of current quantum hardware, and introduces a detailed audit procedure for identifying performance bottlenecks in these complex pipelines. By providing both theoretical definitions and practical Python implementations, this research establishes a robust and reproducible framework for evaluating and optimising hybrid quantum programs, accelerating progress towards practical quantum advantage.

ty constraints for the Utility of Quantumness (UQ), and provide a timing and drift audit for hybrid pipelines. The researchers 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, while preserving matched-budget discipline and reproducibility. Hybrid quantum, classical workflows in the Noisy Intermediate-Scale Quantum (NISQ) era demand more than peak-device metrics, as measures of progress. Teams must track end-to-end maturity, benchmark against strong classical baselines under matched resources, and surface non-obvious bottlenecks.

Benchmarking Quantum-Inspired Algorithms Against Classical Methods

This work presents a comprehensive set of code and documentation for benchmarking and auditing quantum-inspired algorithms against their classical counterparts. The primary goal is to provide a framework for comparing the performance of a classical Simulated Annealing algorithm with a quantum-like greedy heuristic on a Quadratic Unconstrained Binary Optimization problem. The code also tracks the time spent in different stages of the algorithms to identify bottlenecks and understand performance characteristics, employing statistical analysis to assess speedup with confidence intervals. The system generates random QUBO problems, calculates objective function values, and normalizes these values into a quality score for meaningful comparison.

It then implements both the Simulated Annealing algorithm and the quantum-like greedy heuristic, carefully tracking execution time using a utility class to enforce time limits. The code measures the execution time of functions and stores this data for detailed auditing. Performance is evaluated by comparing the speedup at a given quality threshold, and statistical analysis using bootstrap resampling estimates confidence intervals for this speedup. The system identifies the most time-consuming stages of each algorithm, revealing potential areas for optimization. The code is well-structured, documented, and employs statistical rigor, with features for auditing and ensuring reproducibility.

DataFrames are used to store and analyze results effectively. The researchers observe that the quantum-like algorithm is a simplified heuristic and suggest exploring more sophisticated quantum-inspired approaches. They also note that the performance of the algorithms may depend on parameter settings, warranting further investigation. While the code may not scale easily to large problems, the team suggests exploring more efficient algorithms and data structures. Adding visualization capabilities would further enhance the analysis of results. The example usage demonstrates how to generate a QUBO instance, run the algorithms, and calculate confidence intervals for the speedup. In summary, this is a well-designed and well-documented framework for benchmarking and auditing quantum-inspired algorithms, providing a solid foundation for exploring more advanced techniques and data structures.

Quantum Program Maturity and Utility Benchmarking

This research introduces a comprehensive framework for evaluating hybrid quantum programs as complete workflows, moving beyond assessments of isolated components. The QRL score, ranging from 1 to 9, integrates weighted checklist items and calibration drift. The system maps scores on key areas, such as problem formulation and pipeline integration, to determine the overall readiness level.

For example, a project achieving specific scores on these areas, combined with a calibration drift of 12 parts-per-million, receives a QRL score of 3. Improving the integrated pipeline and reducing drift then advances the readiness level. The UQ benchmark measures normalized speedup by comparing the runtime of different solvers at a target quality level. Researchers demonstrated this by evaluating two solvers across three instances. The methodology emphasizes rigorous evaluation, including fixed budgets, strong classical baselines, and detailed reporting of performance distributions.

Scientists advocate for reporting means, confidence intervals, and sensitivity analyses to ensure robust and reproducible results. The team developed a benchmark harness API, including data classes and a BenchmarkRunner class, to facilitate standardized and automated evaluations of hybrid quantum programs. This work delivers a crucial toolkit for assessing the true potential of quantum computing within complex, real-world workflows.

Hybrid Program Maturity and Performance Evaluation

The research team has developed the Hybrid Quantum Program Evaluation Framework (HQPEF), a collection of metrics and tools designed to assess the maturity and performance of hybrid quantum programs as complete workflows. The team also defined a normalized speedup metric, which enables fair benchmarking under quality constraints, and implemented a timing and calibration drift audit for hybrid pipelines, enhancing reproducibility. The utility of the framework is demonstrated through Python reference implementations, allowing researchers to instantiate the metrics and audit procedures with existing quantum and classical solvers while maintaining consistent budgetary constraints. This work shifts the focus from isolated device performance to the overall maturity, fairness, and reproducibility of quantum applications. The authors acknowledge that future work will concentrate on developing more detailed domain-specific quality metrics, improving energy and cost measurement, creating adapters for new hardware, and establishing standardized benchmark suites for regulated deployments.