arxiv: 2604.11781 · v1 · submitted 2026-04-13 · 🪐 quant-ph

Recognition: unknown

Measuring what matters: A scalable framework for application-level quantum benchmarking

Willie Aboumrad , Claudio Girotto , Joshua Goings , Luning Zhao , Miguel Angel Lopez-Ruiz , Daiwei Zhu , Ananth Kaushik , Sayonee Ray

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Samwel Sekwao Jason Iaconis Andrew Arrasmith Andrii Maksymov Yvette de Sereville Felix Tripier Far McKon Coleman Collins Evgeny Epifanovsky Masako Yamada Martin Roetteler

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:22 UTC · model grok-4.3

classification 🪐 quant-ph

keywords quantum benchmarkingapplication-level metricsquantum computing performancebenchmark familiestime-to-solutioncross-platform comparisonscalable frameworkquantum workloads

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The pith

A scalable framework with 13 benchmark families enables application-level evaluation and cross-platform comparison of quantum computing systems.

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

The paper aims to create a benchmarking approach that focuses on meaningful, application-level metrics instead of just hardware specifications. It introduces a framework based on principles of measurability, simplicity, scalability, and extensibility, featuring 13 families of benchmarks drawn from realistic workloads in multiple domains. This matters because it helps bridge the gap between low-level quantum performance data and practical value for users and developers. By measuring solution quality, total execution time, energy consumption, and time-to-solution, the benchmarks aim to be reproducible and interpretable. If the framework works as intended, it could help establish industry standards for assessing quantum systems.

Core claim

We present a scalable framework for application-level quantum benchmarking that is designed to support internal system evaluation and cross-platform comparison across technology providers. Our framework is guided by a set of core principles, including measurability, simplicity, scalability, and extensibility. We present 13 benchmark families that reflect realistic workloads across multiple domains. This enables the systematic evaluation of the quality of solutions, the total execution time, total used energy, as well as Time-to-Solution. The benchmarks are designed to be reproducible, interpretable across stakeholder groups, and adaptable to evolving system capabilities. The framework aims t

What carries the argument

The 13 benchmark families reflecting realistic workloads, which carry the argument by allowing measurement of application-level metrics such as solution quality, execution time, energy use, and time-to-solution.

Load-bearing premise

That the chosen 13 benchmark families sufficiently represent meaningful real-world workloads and can be implemented in a reproducible and interpretable way across different and evolving quantum platforms.

What would settle it

If running the benchmarks on multiple quantum platforms yields results that do not correlate with actual performance in real applications or if the benchmarks prove difficult to implement consistently across platforms.

Figures

Figures reproduced from arXiv: 2604.11781 by Ananth Kaushik, Andrew Arrasmith, Andrii Maksymov, Claudio Girotto, Coleman Collins, Daiwei Zhu, Evgeny Epifanovsky, Far McKon, Felix Tripier, Jason Iaconis, Joshua Goings, Luning Zhao, Martin Roetteler, Masako Yamada, Miguel Angel Lopez-Ruiz, Samwel Sekwao, Sayonee Ray, Willie Aboumrad, Yvette de Sereville.

**Figure 1.** Figure 1: Illustration of execution time as reported in the framework. Shown in a) is the execution view at source ↗

**Figure 2.** Figure 2: VQE benchmark performance for hydrogen chains at view at source ↗

**Figure 3.** Figure 3: Performance of the fixed-angle QAOA benchmark for solving the MaxCut problem on view at source ↗

**Figure 4.** Figure 4: Performance of the LR-QAOA benchmark for solving the MaxCut problem on 3- and view at source ↗

**Figure 5.** Figure 5: A quantum circuit implementing 5 layers of Fixed Point Amplitude Amplification (FAA) view at source ↗

**Figure 6.** Figure 6: Performance of the Fixed Point Amplitude Amplification (FAA) benchmark for unstruc view at source ↗

**Figure 7.** Figure 7: A typical QCNN circuit. Once the data is loaded onto the qubit register, it is transformed view at source ↗

**Figure 8.** Figure 8: Test accuracy of the 4 QCNN problem instances with 50 test images. view at source ↗

**Figure 9.** Figure 9: QCBM ansatz 1 used to model a 2-variable copula with 3 bits per variable. view at source ↗

**Figure 10.** Figure 10: QCBM ansatz 2 used to model a 2-variable copula with 3 bits per variable. view at source ↗

**Figure 11.** Figure 11: Benchmark scores versus number of portfolio variables for QCBM ansatz 1 (left) and 2 view at source ↗

**Figure 12.** Figure 12: The tensor network diagram used to represent a 2D image view at source ↗

**Figure 13.** Figure 13: The Mean-Squared error score of the MNIST images (left) and the ImageNet-Sketch view at source ↗

**Figure 16.** Figure 16: A typical Cosine QFT Challenge circuit. The circuit first loads a cosine plane wave of a view at source ↗

**Figure 17.** Figure 17: A typical Hidden Phase QFT challenge circuit. The circuit first loads a superposition view at source ↗

**Figure 18.** Figure 18: Benchmark score for the Cosine QFT challenge obtained using IonQ Forte and IonQ view at source ↗

**Figure 19.** Figure 19: Benchmark score for the Hidden Phase QFT challenge. view at source ↗

**Figure 20.** Figure 20: Structure of a hidden shift circuit. The objective is to recover the hidden shift view at source ↗

**Figure 21.** Figure 21: Benchmark scores computed on IonQ Forte class systems for the HSBP challenge fam view at source ↗

**Figure 22.** Figure 22: Benchmark scores obtained on IonQ Forte when executing HSBP random permutation view at source ↗

**Figure 23.** Figure 23: Benchmark score for varQITE challenges testing the solution quality of MaxCut prob view at source ↗

**Figure 24.** Figure 24: QC-AFQMC results for linear H4 in STO-3G at 2.0 Å using CAS(4,4) (8 qubits). (a) Imaginary-time evolution of the total energy; the dashed line marks the FCI reference (−1.898 Ha). (b) Final reblocked energies for the ideal simulator and the Aria-1, Forte-1, and Forte-Enterprise-1 QPUs; error bars denote 95% confidence intervals (numbers in parentheses indicate the corresponding uncertainty in the last dig… view at source ↗

**Figure 25.** Figure 25: QC-AFQMC results for linear H6 in STO-3G at 2.0 Å using CAS(6,6) (12 qubits). (a) Imaginary-time evolution of the total energy; the dashed line marks the FCI reference (−2.847 Ha). (b) Final reblocked energies for the ideal simulator and the Forte-1 and Forte-Enterprise-1 QPUs; error bars denote 95% confidence intervals (numbers in parentheses indicate the corresponding uncertainty in the last digits). 45 view at source ↗

**Figure 26.** Figure 26: Overview of the QLBM pipeline, showing the D2Q5 lattice Boltzmann formulation to view at source ↗

**Figure 27.** Figure 27: The overlap, |⟨ψ|ϕ⟩|, between the QPU density and the exact classical LBM solution (orange curve). Further improvement in fidelity is achieved by measuring observables (instead of doing full state tomography from raw shots) and reconstructing the 2D Gaussian distribution, where fidelity remains ∼ 94% even after 10 time steps (blue curve). 4. Macroscopic quantities: The final step involves applying Hadamar… view at source ↗

**Figure 28.** Figure 28: Lepton number time evolution during |∆−∆−⟩ two-baryon state decay in 1+1D QCD. The circuit used two first-order Trotter steps with approximate valence-fermion weak interactions (470 two-qubit gates). Panels show results for mM = 1.7 (left), mM = 0 (center), and the difference between them (right). Data was obtained from IonQ Forte Enterprise. Black points indicate ideal simulations; the gray dotted line i… view at source ↗

**Figure 29.** Figure 29: Time-to-solution for the HSBP challenge at 36 qubits with permutations consisting of view at source ↗

**Figure 30.** Figure 30: Time-to-solution for the Cosine QFT challenge at 36 qubits. This plot illustrates that view at source ↗

**Figure 31.** Figure 31: Shown are the quasi-probability distributions of the Hamming weight of bitvectors for view at source ↗

**Figure 32.** Figure 32: Time-to-solution (TTS) at 99% confidence as a function of the minimum approximation view at source ↗

**Figure 33.** Figure 33: Shown are the quasi-probability distributions of the approximation ratio of bitvectors view at source ↗

read the original abstract

As quantum computing systems continue to mature, there is an increasing need for benchmarking methodologies that capture performance in terms of meaningful, application-level metrics. In this work, we present a scalable framework for application-level quantum benchmarking that is designed to support internal system evaluation and cross-platform comparison across technology providers. Our framework is guided by a set of core principles, including measurability, simplicity, scalability, and extensibility. We present 13 benchmark families that reflect realistic workloads across multiple domains. This enables the systematic evaluation of the quality of solutions, the total execution time, total used energy, as well as Time-to-Solution. The benchmarks are designed to be reproducible, interpretable across stakeholder groups, and adaptable to evolving system capabilities. The framework aims to bridge the gap between low-level performance metrics and real-world value, providing a unified approach to assessing quantum systems. The resulting benchmarks support development and validation and contribute to the foundation of industry-wide benchmarking standards.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A clear but untested proposal for 13 quantum benchmark families that organizes metrics around real workloads without showing any runs or validation.

read the letter

This paper puts forward a framework for application-level quantum benchmarking built on 13 benchmark families and four main metrics: solution quality, execution time, energy, and time-to-solution. The authors lay out four design principles—measurability, simplicity, scalability, and extensibility—and sketch how each family maps onto them. That structure is the main new piece; it tries to shift attention from gate counts or circuit depth toward workloads that look more like chemistry, optimization, or ML tasks. The intent to support both internal tuning and cross-platform comparisons is reasonable and timely. The abstract and description stay consistent with those goals and avoid overclaiming theorems or scaling laws. The stress-test note is right that the adequacy of the families is treated as a design choice rather than a tested assertion. The soft spot is the complete absence of any implementation details, sample code, or empirical results. Without even a small demonstration on current hardware, it is hard to judge whether the families are actually reproducible across vendors or whether the energy and time-to-solution numbers can be collected cleanly. The choice of exactly 13 families also sits there without much external justification from usage surveys or domain experts. This is the kind of paper that belongs in a reading group for people who work on quantum software stacks or standards efforts. A practitioner looking for a concrete list of tasks to try will find something to react to, even if they end up modifying it. It is not a finished method yet, but the topic matters enough that it should go to peer review so referees can ask for examples and comparisons to existing suites.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes a scalable framework for application-level quantum benchmarking guided by principles of measurability, simplicity, scalability, and extensibility. It introduces 13 benchmark families reflecting realistic workloads across multiple domains and defines associated metrics including solution quality, execution time, energy consumption, and Time-to-Solution. The framework is intended to enable reproducible, interpretable evaluations for internal system assessment and cross-platform comparisons, bridging low-level hardware metrics to application-level value.

Significance. If implemented as described, the framework could help standardize application-oriented benchmarking in quantum computing, addressing the current emphasis on low-level metrics and supporting more relevant cross-technology evaluations. The multi-metric approach including energy and time-to-solution is a positive step toward assessing practical utility.

minor comments (2)

[Abstract] Abstract: The claim that the 13 benchmark families 'reflect realistic workloads across multiple domains' would be strengthened by a brief high-level categorization or list of domains in the abstract or introduction to allow immediate assessment of coverage.
The manuscript would benefit from an explicit discussion (perhaps in a dedicated section) of how the design principles were applied in selecting and defining the benchmark families to ensure the choices are transparent.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript, including the recognition of its potential to standardize application-oriented benchmarking and the multi-metric approach. The report recommends minor revision but does not list any specific major comments. Accordingly, we have no individual points to address in this rebuttal and are prepared to handle any minor editorial or clarification requests during revision.

Circularity Check

0 steps flagged

No significant circularity: methodological framework with no derivations or predictions

full rationale

The manuscript is a design proposal for an application-level quantum benchmarking framework. It defines 13 benchmark families, states four guiding principles (measurability, simplicity, scalability, extensibility), and describes how each family maps to metrics such as solution quality, execution time, energy, and Time-to-Solution. No equations, scaling laws, fitted parameters, uniqueness theorems, or empirical predictions are asserted. The choice of families is presented explicitly as a design decision rather than a derived or validated result. Consequently there is no derivation chain that can reduce to its own inputs, no self-citation load-bearing step, and no renaming of known results as new predictions. The work is self-contained as a methodological contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The proposal rests on domain assumptions about benchmark representativeness without independent evidence or validation provided in the abstract.

axioms (1)

domain assumption The 13 benchmark families reflect realistic workloads across multiple domains.
Invoked in the abstract as the basis for the framework design.

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