Quantum Fidelity-per-Cost: A Metric for Evaluation of Quantum Computing Systems
2026-07-30 • Emerging Technologies
Emerging TechnologiesPerformance
AI summaryⓘ
The authors studied different cloud-based quantum computers from four providers to see how well they perform and how much they cost. They created a new score called Quantum Fidelity-per-Cost (QFC) that combines how accurate the computer’s results are and how much it costs to run them. They found that when price is considered alongside accuracy, the best choice of quantum computer can change. This means users might pick different machines if they care about cost, not just performance. The authors also showed that pricing models affect these scores more than the hardware itself.
Quantum computingQuantum fidelityCloud computingQuantum processing units (QPU)Kullback-Leibler divergenceShot countBilling modelQuantum RuntimeCost-aware metricsQuantum hardware performance
Authors
Siddarth Shinde, Jakub Szefer
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.