Re-examining the quantum volume test: Ideal distributions, compiler optimizations, confidence intervals, and scalable resource estimations
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The quantum volume test is a full-system benchmark for quantum computers that is sensitive to qubit number, fidelity, connectivity, and other quantities believed to be important in building useful devices. The test was designed to produce a single-number measure of a quantum computer's general capability, but a complete understanding of its limitations and operational meaning is still missing. We explore the quantum volume test to better understand its design aspects, sensitivity to errors, passing criteria, and what passing implies about a quantum computer. We elucidate some transient behaviors the test exhibits for small qubit number including the ideal measurement output distributions and the efficacy of common compiler optimizations. We then present an efficient algorithm for estimating the expected heavy output probability under different error models and compiler optimization options, which predicts performance goals for future systems. Additionally, we explore the original confidence interval construction and show that it underachieves the desired coverage level for single shot experiments and overachieves for more typical number of shots. We propose a new confidence interval construction that reaches the specified coverage for typical number of shots and is more efficient in the number of circuits needed to pass the test. We demonstrate these savings with a Q V = 2 10 experimental dataset collected from Quantinuum System Model H1-1. Finally, we discuss what the quantum volume test implies about a quantum computer's practical or operational abilities especially in terms of quantum error correction.
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Detailed modeling predicts that the SPAM error is dominated by measurement error for 171Yb+.
“however, detailed modeling predicts that the SPAM error is dominated by measurement error for 171 Yb þ [47,48].”
From A Race-Track Trapped-Ion Quantum Processor (2023) · cites this paper as [47]
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A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation
Niu, S., Kökcü, E., Johri, S., Ramesh, A., Chatterjee, A., Neira, D. E. B., Camps, D., & Lubinski, T. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00050 · Cited by 2 · see citing works
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Counting collisions in random circuit sampling for benchmarking quantum computers
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Qubit Condensation for Assessing Efficacy of Molecular Simulation on Quantum Computers
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Quantum data generation in a denoising model with multiscale entanglement renormalization network
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An Overview of Practical Classical Computing
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STRESS TESTING IBM_BRISBANE: Rapid Noise Saturation with Deep Random Circuits on 127 QubitsPreprint
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- Authors
- Baldwin, C. H., Mayer, K., Brown, N. C., Ryan-Anderson, C., & Hayes, D.
- Affiliations
- Quantinuum (United States) (US)
- Venue
- Quantum
- Published
- 2022-05-09
- Volume
- 6
- Pages
- 707
- ISSN
- 2521-327X (ISSN-L)
- OpenAlex
- W3208153744
- Cited by
- 54
- References
- 72
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 15 · 2023: 11 · 2022: 3
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Quantum (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- quantum volume, confidence intervals, compiler optimization, resource estimation, quantum error correction, qubit number
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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