Quantum Fidelity-per-Cost: A Metric for Evaluation of Quantum Computing Systems

2026-07-30

AI Summary

This study addresses the lack of cross-platform evaluation methods for quantum computing that jointly account for computational fidelity and cost, a gap that hinders optimal hardware selection under heterogeneous cloud pricing models. The authors introduce, for the first time, a “Quantum Fidelity per Cost” (QFC) metric that integrates Kullback–Leibler divergence, sampling overhead, and actual platform fees. They empirically evaluate 14 quantum processors across AWS, IBM, IQM, and OQC using this metric. Their findings reveal that incorporating cost substantially alters rankings based solely on fidelity; QFC remains stable under reweighting, and its scaling behavior is governed primarily by billing models rather than hardware characteristics. Furthermore, QFC dynamically adapts to device availability and price changes, offering users a practical, cost-aware decision framework for quantum hardware selection.

Abstract

Cloud-accessible quantum computing has made hardware comparison not only a physics benchmark but also a practical purchasing decision. Cost-aware comparison of quantum computers remains underexplored and is difficult to do under the heterogeneous billing models offered by various cloud-based quantum computing providers. This paper makes two main contributions to enable price-aware comparison of quantum computers. First, this work presents a cross-provider measurement study of quantum circuit execution fidelity spanning 14 cloud QPU access-path entries (12 distinct physical QPUs) across four cloud access paths: Amazon Web Services (AWS) cloud, IBM Quantum Runtime (IBM) cloud, IQM Resonance (IQM) cloud, and Oxford Quantum Circuits (OQC) cloud. Second, this work proposes and analyzes a cost-aware score, Quantum Fidelity-per-Cost (QFC), which combines Kullback--Leibler (KL) divergence from an ideal output distribution, shot count, and monetary cost into one possible metric under a documented billing model. The main empirical observation from this work is that cost-aware ranking can differ from purely fidelity-based evaluation of quantum computers, and that users may select different quantum computing backends when they consider price in their selection, as opposed to selection based on fidelity alone. This work shows that the ranking is stable under reweighting of the metric, and that a device's billing model, not its hardware, governs how its score scales with shot count. Reported QFC values change as new machines come online or as providers revise their prices.

Problem

• quantum computing
• cost-aware comparison
• fidelity
• cloud quantum hardware
• billing models

Innovation

• Quantum Fidelity-per-Cost
• cost-aware benchmarking
• cloud quantum computing
• KL divergence
• cross-provider evaluation