GroverFigureOfMerit: An Agnostic Figure of Merit for Quantum Backend Characterization in the NISQ Era

• URL: http://arxiv.org/abs/2607.08636v1
• Date: Thu, 09 Jul 2026 16:08:48 GMT
• Title: GroverFigureOfMerit: An Agnostic Figure of Merit for Quantum Backend Characterization in the NISQ Era
• Abstract summary: We propose a Figure of Merit based on Grover's algorithm as an algorithmic stress test evaluating quantum backend performance holistically.<n>The metric combines success probability on target states with penalties for non-uniform amplification and leakage to non-marked states, yielding a unified score across hardware architectures.
• Score: 41.99844472131922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Noisy Intermediate-Scale Quantum (NISQ) era poses a challenge for developers: hardware providers expose capabilities through heterogeneous interfaces with proprietary metrics varying widely across providers, hindering informed backend selection. Static characterization metrics - coherence times T1/T2, gate error rates - exhibit limitations: they fail to capture dynamic variability across successive executions, overlook the impact of transpilation, and lack architectural comparability across physically distinct technologies. We propose a Figure of Merit (FoM) based on Grover's algorithm as an algorithmic stress test evaluating quantum backend performance holistically. The metric combines success probability on target states with penalties for non-uniform amplification and leakage to non-marked states, yielding a unified score across hardware architectures. Implemented via the Qonscious framework - a conditional execution platform using polymorphic adapters, it executes agnostically on IBM, IonQ backends, and simulators. Main contributions: (1) proposal and validation of GroverFigureOfMerit, incorporating uniformity and leakage penalties (adapted from GRADE) with emphasis on noise, transpilation, and topological constraints; (2) systematic analysis of heterogeneity across nine quantum providers motivating agnostic metrics; and (3) experimental demonstration via ideal simulators and real-processor noise models, confirming sensitivity to noise, topology, and transpilation overhead. Results confirm the metric distinguishes backend performance under a unified score, capturing intrinsic algorithmic limits. Validation on physical QPUs is identified as a natural next step.

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