Quantum Computer Benchmarking: An Explorative Systematic Literature Review

Claudia Linnhoff-Popien; Federico Harjes Ruiloba; Jonas Stein; Sabrina Egger; Sebastian von Beck; Tobias Rohe

REVIEW 2 major objections 4 minor 3 cited by

Quantum Computer Benchmarking: An Explorative Systematic Literature Review

T0 review · 2 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This review sorts the whole field of gate-based quantum-computing benchmarks—329 studies—into one stack-aligned taxonomy, and claims the result is a shared language for comparing quantum systems.

desk verdict A well-executed systematic review whose taxonomy is the real contribution; fix the reproducibility gaps and temper the 'most comprehensive' claim. read the letter →

arxiv 2509.03078 v1 pith:OSKBGEI4 submitted 2025-09-03 quant-ph

Tobias Rohe , Federico Harjes Ruiloba , Sabrina Egger , Sebastian von Beck , Jonas Stein , Claudia Linnhoff-Popien This is my paper

classification quant-ph

keywords quantumcomputingbenchmarkingsystematicliteraturereviewtaxonomystackhardwarefocussoftwareapplication

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that quantum-computing benchmarking is fragmented: many protocols exist, few definitions agree, and no single benchmark covers everything. It claims to be the most comprehensive systematic literature review of gate-based QC benchmarking to date—329 studies—and to solve the fragmentation by introducing a three-part taxonomy: hardware focus, software focus, and application focus, aligned with the quantum stack and with the people who use benchmarks. If the taxonomy is right, benchmark designers, hardware vendors, and application users get a common vocabulary, a map of what exists, and a clear view of where the gaps are. The review also claims that benchmark categories are interdependent, so progress claims should be checked across the whole stack rather than on a single metric.

What carries the argument

The taxonomy itself is the central object. It is built by combining BERTopic-based natural-language clustering of 329 papers with expert refinement, then aligning categories with the layers of the quantum stack (application, algorithm, programming language, compiler/runtime, instruction set, microarchitecture, quantum-classical interface, quantum chip). Each benchmark is assigned first by intended stakeholder and stack layer, then by methodological principle; the definitions and the mapping of interdependencies are what carry the argument. The adopted benchmark definition from ref. [12] sets the boundary: a benchmark is a test measuring performance of a quantum processor or hardware componen

What would settle it

Have two independent panels of QC researchers apply the taxonomy's definitions to a random sample of, say, 100 benchmark papers from the 329 in the review and measure inter-rater agreement. If agreement on the top-level hardware/software/application assignment is at or below chance (e.g., Cohen's kappa below 0.6), the taxonomy fails to provide the unambiguous common language it claims. Alternatively, a single well-known gate-based benchmark that the taxonomy cannot place under any subcategory would falsify its completeness.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that the landscape of gate-based quantum-computer benchmarking can be organized into a single hierarchical taxonomy: three primary categories—hardware focus, software focus, application focus—that mirror the lower, middle, and upper layers of the quantum stack, each subdivided by methodological principle (component-level metrics, tomography, randomized benchmarks, volumetric benchmarks, algorithm-based benchmarks, error-correction benchmarks, ground-state energy calculations, quantum machine learning, quantum optimization). It claims these categories come with precise definitions, that interdependencies between them (gate fidelity to algorithm s

Load-bearing premise

The taxonomy assumes every gate-based QC benchmark can be placed in exactly one of the three focus categories using primary stakeholder and stack layer, with borderline assignments being rare enough not to undermine the classification; the paper itself concedes that these assignments carry a degree of subjectivity.

Editorial extensions

If this is right

A common vocabulary lets competing benchmark papers be compared on what they actually measure rather than on their names.
Hardware vendors and researchers can see which benchmark categories serve which decisions, and which categories are missing from current practice.
The interdependence map implies that improving one layer, such as gate fidelity, has predictable effects upward, so progress reports can be checked for stack-wide consistency.
Fairer evaluation becomes possible because categories and definitions expose hidden choices like compiler sensitivity, not just device quality.
Research gaps become explicit: composite cross-level benchmarks that link low-level metrics to application outcomes are identified as an open direction.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The taxonomy's usefulness depends on how reliably independent experts assign borderline benchmarks to the same category; an inter-rater reliability test on a sample of the 329 papers would make the claim to a common language checkable.
If the taxonomy were applied to non-gate-based platforms such as quantum annealers or analog simulators, the categories might need new top-level entries, suggesting the three-category structure is tied to the gate-based model.
The paper's own interdependence analysis implies that single-number vendor metrics like quantum volume or Q-score should be read as partial views; a device that scores high on one category may still fail on application benchmarks, and that is informative, not contradictory.
A natural extension is a living repository that continuously re-classifies new benchmarks; if new protocols repeatedly straddle the existing subcategories, the taxonomy would need revision, which the paper already anticipates.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a solid, useful systematic review of gate-based QC benchmarking, and the taxonomy it proposes is the main contribution. The paper is not a new measurement or algorithm; it is organizational work, and it is good organizational work. If you work in benchmarking, the three-level hardware/software/application split with subcategories like dequantization, error-correction, and compiler benchmarks is a genuinely useful way to navigate the literature. The technical descriptions of the benchmarks (T1/T2, RB, XEB, Q-score, tomography variants) are accurate in the places I checked. The search process is described in detail, and the authors are transparent about their screening decisions.

The soft spots are real but manageable. First, the 'most comprehensive systematic literature review to date' claim is not supported by evidence. The authors report 329 studies considered and 273 cited, but they do not provide the study list, the search protocol details, or the NLP clustering code and data. For a systematic review, that is a reproducibility gap. A reader cannot verify completeness or re-run the analysis. This should be fixed before publication.

Second, the taxonomy's boundary conditions are acknowledged to be subjective. The stress-test concern about inter-rater reliability is fair: the definitions of Software Focus and Application Focus overlap for algorithm-driven benchmarks like Q-score and VQE. That said, I don't think this is fatal. The top-level categories are broad, and the paper's own examples are defensible. Q-score is explicitly a full-stack application benchmark; VQE ground-state energy is an application problem. The subjectivity is in the borderline cases, and the paper admits it. A reliable taxonomy would benefit from an inter-rater agreement study, but its absence does not sink the contribution. It just means the 'common language' claim should be tempered to 'a proposed common language.'

Citation pattern looks fine. The single self-citation [197] is used as an example of an optimization workflow, not as a load-bearing source.

Bottom line: this paper deserves a serious referee. It is a useful reference for anyone entering QC benchmarking or wanting a structured overview. I would accept it for peer review with requests for the study list, clustering artifacts, and a more modest claim about comprehensiveness. The core taxonomy is sound and likely to be adopted.

Referee Report

2 major / 4 minor

Summary. This paper presents a systematic literature review of benchmarking for gate-based quantum computers. The authors searched ACM Digital Library, Google Scholar, and ProQuest (412 initial records, 329 studies after screening and forward-backward search), used BERTopic clustering plus manual refinement to organize the literature, and propose a three-level taxonomy — Hardware Focus, Software Focus, Application Focus — with subcategories such as tomography, randomized benchmarks, volumetric benchmarks, compiler benchmarks, ground-state energy calculations, quantum machine learning, and quantum optimization. They define benchmark characteristics, map interdependencies across stack layers, and identify research gaps. The paper claims to be the most comprehensive systematic review of QC benchmarking to date and to provide a common language for the field.

Significance. If the taxonomy is stable and reproducible, this would be a valuable reference: the review is more systematic than most prior narrative reviews, the technical descriptions of randomized benchmarking, XEB, GST, quantum volume, Q-score, and VQE benchmarks are largely faithful to the underlying literature, and the stakeholder/stack alignment gives practitioners a useful map. The paper also provides a transparent search protocol with inclusion/exclusion counts, a forward-backward search, and NLP clustering details in Appendix A, which strengthens confidence in coverage. However, the value of the central contribution depends on the boundary assignments being trustworthy; that is currently not demonstrated.

major comments (2)

[Secs. 5.2–5.3 and Sec. 7] The central claim is that the taxonomy provides a 'common language' for QC benchmarking, but category assignments are not shown to be reproducible. Sec. 5.2 defines Software Focus as 'system-wide performance' and says it is 'also relevant for hardware developers seeking to assess and compare their hardware as a holistic system'; Sec. 5.3 places Q-score and VQE ground-state benchmarks in Application Focus even though these are algorithm-driven and could satisfy the Software Focus definition of 'evaluating... algorithms and frameworks.' Sec. 7 concedes that 'the categorization and interpretation of borderline cases inherently involve a degree of subjectivity,' but no inter-rater reliability measure (e.g., Cohen's kappa) or explicit decision rule is reported. Because a shared vocabulary depends on stable assignment of benchmarks to categories, this is load-bearing. Please add a coding proto
[Sec. 2.1 vs. Secs. 5.2.4/5.2.6] The benchmark definition adopted from Acuaviva et al. restricts benchmarks to tests that evaluate 'a quantum processor or hardware component for a certain task.' The review then includes compiler benchmarks (Sec. 5.2.6) and dequantization benchmarks (Sec. 5.2.4), which evaluate software and algorithmic components rather than a quantum processor or hardware component. This makes the foundational definition inconsistent with the taxonomy's Software and Application Focus categories. Please either broaden the benchmark definition, or explicitly show how compiler and dequantization evaluations fit the adopted definition; otherwise the proposed 'standard terminology' is not internally coherent.

minor comments (4)

[Sec. 5.2.1, Eq. (11)] The quantum volume equation is miswritten: it uses 'arg max' where the expression should be a maximum. The correct form is log2 VQ = max_m min(m, d(m)), and d(m) denotes the achievable circuit depth for width m, not 'the number of qubits in the largest square circuit' as stated in the text. Please correct the notation.
[Abstract and Sec. 3, final paragraph] The claim of 'the most comprehensive systematic literature review to date' is not substantiated by a quantitative or qualitative comparison with the cited prior reviews [12,37,38] on search coverage, time span, inclusion criteria, or number of included studies. Please provide such a comparison or soften the claim to avoid an unsupported superlative.
[Sec. 4.2] The final search string requires 'benchmark*' in the abstract and may miss relevant papers that use terms such as 'characterization,' 'evaluation,' or 'validation' without explicitly mentioning benchmarks. The forward-backward search mitigates this, but a sentence acknowledging the residual limitation would strengthen the methodology discussion.
[Sec. 4.3] The phrase 'double-blind setting' is ambiguous: the text describes two reviewers independently applying inclusion/exclusion criteria, which is not the usual meaning of double-blind review. Consider using 'dual independent screening' to avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: literature review taxonomy is a synthesis, not a derivation; the single self-citation [197] is not load-bearing.

full rationale

This is a systematic literature review, so there is no mathematical derivation chain whose output could be equivalent to its input by construction. The taxonomy is produced from the reviewed corpus (141 initial studies plus forward/backward search) through BERTopic clustering and manual expert refinement, then organized into Hardware/Software/Application Focus categories aligned with the QC stack. That is a synthesizing and organizational activity, not a prediction from fitted parameters. The only self-citation, [197], appears in Sec. 5.3 as an example of 'practical QC workflows'; it is not used to justify the taxonomy, to define categories, or to force any classification, so it is not load-bearing. The definitions and equations in the paper (e.g., T1/T2 decays, fidelity formulas, quantum volume, Q-score) are standard results cited from the literature and are presented as descriptions, not as predictions derived from the taxonomy. The paper explicitly acknowledges in Sec. 7 that 'the categorization and interpretation of borderline cases inherently involve a degree of subjectivity,' which is a transparency statement about expert judgment, not evidence of circularity. No self-definitional step, fitted-input-called-prediction step, uniqueness-imported-from-authors step, ansatz-smuggled-in-via-citation step, or renaming-known-result step is present. The central claim is a framework built from external sources plus declared expert interpretation, so the derivation chain is self-contained in the sense appropriate to a literature review.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The review introduces no new free parameters or invented entities. Its load-bearing assumptions are the adopted benchmark definition, the QC stack as the organizing axis, the gate-based scope restriction, and the validity of the NLP clustering as a starting point for the taxonomy.

assumptions (4)

domain assumption A QC benchmark is 'a test, or set of tests, that aims to measure or evaluate the performance, efficiency, or other properties of a quantum processor or hardware component for a certain task' (Acuaviva et al., Sec. 2.1).
Adopted from an external source as the scope-defining definition of what counts as a benchmark for inclusion in the review.
domain assumption The QC stack, from application down to the quantum chip, is the correct organizing backbone for categorizing benchmarks.
Used throughout Sec. 5 to assign benchmarks to hardware, software, and application focus categories. If the stack is not the natural organizing axis, the taxonomy's partitions lose their grounding.
domain assumption Only universal, gate-based QC is in scope; quantum annealers and analog simulators are excluded.
Stated as an inclusion/exclusion criterion in Sec. 4.3 and acknowledged as a limitation in Sec. 7, but it shapes the claimed completeness of the review.
domain assumption The NLP-based BERTopic clustering provides a meaningful initial structure that can be refined by manual expert analysis.
The clustering step is described in Appendix A, but the validity of the resulting clusters is not quantitatively assessed, and the taxonomy relies on the authors' manual refinement to correct any cluster misassignments.

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Cite this review

Pith. "Pith review of Quantum Computer Benchmarking: An Explorative Systematic Literature Review." pith.science (2026). https://pith.science/paper/OSKBGEI4

@misc{pith2026250903078,
  author       = {Pith},
  title        = {Pith review of: Quantum Computer Benchmarking: An Explorative Systematic Literature Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OSKBGEI4}},
  note         = {Machine review of arXiv:2509.03078}
}

read the original abstract

As quantum computing (QC) continues to evolve in hardware and software, measuring progress in this complex and diverse field remains a challenge. To track progress, uncover bottlenecks, and evaluate community efforts, benchmarks play a crucial role. But which benchmarking approach best addresses the diverse perspectives of QC stakeholders? We conducted the most comprehensive systematic literature review of this area to date, combining NLP-based clustering with expert analysis to develop a novel taxonomy and definitions for QC benchmarking, aligned with the quantum stack and its stakeholders. In addition to organizing benchmarks in distinct hardware, software, and application focused categories, our taxonomy hierarchically classifies benchmarking protocols in clearly defined subcategories. We develop standard terminology and map the interdependencies of benchmark categories to create a holistic, unified picture of the quantum benchmarking landscape. Our analysis reveals recurring design patterns, exposes research gaps, and clarifies how benchmarking methods serve different stakeholders. By structuring the field and providing a common language, our work offers a foundation for coherent benchmark development, fairer evaluation, and stronger cross-disciplinary collaboration in QC.

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Forward citations

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