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Resource-Optimal Screening for Two-Tier Quantum System Characterization

Quantum processing units (QPUs) require frequent benchmarking to track hardware drift, yet standard protocols such as randomized benchmarking are resource-intensive and compete with application workloads for scarce QPU…

By Chen, Dalvi, Quetschlich

Score████░░░░░░4.2

Key numbers

  • 4× in detection rate at

Caveats

  • Preprint; not yet peer reviewed.

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Abstract

Quantum processing units (QPUs) require frequent benchmarking to track hardware drift, yet standard protocols such as randomized benchmarking are resource-intensive and compete with application workloads for scarce QPU time. We present a resource-optimal screening strategy that combines a two-tier, risk-based testing architecture with optimal experiment design: a lightweight screening probe runs regularly and triggers expensive full characterization only when statistically significant change is detected, while the probe itself is designed to maximize detection sensitivity under explicit resource constraints. For an exponential fidelity model, we derive closed-form optimal circuit sizes and introduce an effective gate count that renders the analysis hardware-independent. We instantiate the strategy for two applications. For gate-quality verification, we show that optimally designed screening probes outperform randomized benchmarking with optimal experimental design (OED) by more than 4× in detection rate at equal resource cost. For compiler benchmarking, we formalize compiler comparison as a paired hypothesis test and derive the optimal circuit size for distinguishing compilation strategies. Despite their different observables and test statistics, both applications yield structurally the same closed-form expression for the optimal probe circuit size as a function of gate fidelity, illustrating the generality of the framework. Numerical and hardware experiments on QPUs (Rigetti Ankaa-3, IQM Garnet, IQM Emerald) confirm the theoretical predictions, with observed optimal circuit sizes matching the derived values across all devices.

Yi-Ting Chen, Aniket S. Dalvi, Nils Quetschlich

The editor's rubric

Heuristic review

Leverage3 / 5
A method or resource many groups across the field will adopt within a year.
18% of this desk’s rubric
Magnitude3 / 5
Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
20% of this desk’s rubric
Evidence3 / 5
Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
22% of this desk’s rubric
Novelty2 / 5
A new combination of known ideas.
22% of this desk’s rubric
Trajectory2 / 5
Some room to improve with obvious engineering.
10% of this desk’s rubric
Stakes2 / 5
Benefits a professional community (practitioners, clinicians, engineers).
8% of this desk’s rubric

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose, we report); breadth (many tasks); gains (x-fold, outperforms); design (randomized).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.2

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit
5.2 / 10
Weighted rubric, evidence-gated.
Adjusted merit
4.5 / 10
Shrunk toward the desk prior by editor confidence (42%).
Attention
0%
Citations, upvotes, points, mentions.
Freshness
80%
Half-life decay since publication.

No attention signals recorded yet.

The record

  • Reviewed by heuristic-v10 on Oct 27, 2026, 07:29 UTC. Paper type: resource.
  • Categories: quant-ph
  • BRIEF, No.3 in the Physics edition of October 27, 2026.