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This research introduces Quantum Fidelity-per-Cost (QFC), a novel metric that integrates execution fidelity, shot count, and monetary expense to enable practical cross-provider comparisons of cloud quantum hardware. The study reveals that cost-aware ranking often diverges from pure performance benchmarks, suggesting that billing models significantly influence backend selection more than raw device capabilities alone. For practitioners evaluating cloud QPU access paths, this framework offers a stable method for optimizing resource allocation across heterogeneous providers like AWS, IBM, IQM, and OQC.
Quantum Fidelity-per-Cost: A Metric for Evaluation of Quantum Computing Systems
arXiv:2607.28572v1 Announce Type: new 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.
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.
7 pages, 4 figures, 3 tables. Accepted at IEEE International Conference on Quantum Computing and Engineering (QCE) 2026
Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Performance (cs.PF)
arXiv:2607.28572 [quant-ph]
(or arXiv:2607.28572v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2607.28572 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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