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arxiv: 2605.02861 · v1 · submitted 2026-05-04 · 🪐 quant-ph · cs.ET

Opportunities and challenges in scaling quantum error detection on hardware

Yanis Le Fur , Ethan Egger , Hong-Ye Hu , Vincent Russo , William J. Zeng , Ryan LaRose This is my paper

Pith reviewed 2026-05-09 15:59 UTC · model grok-4.3

classification 🪐 quant-ph cs.ET
keywords quantum error detectionrepetition codetriangular color codequantum hardwareerror mitigationpseudothresholdscaling
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The pith

Quantum error detection yields exponentially improving results with code distance on current hardware.

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

The paper benchmarks quantum error detection on real and simulated devices using repetition and triangular color codes for both memory storage and logical operations. It shows that post-selected expectation values converge exponentially toward the noiseless case as code distance grows, even with circuits involving up to 74 physical qubits. Sample counts rise exponentially with noise level while classical decoding and embedding costs remain constant but large, yet pseudothreshold estimates indicate regions where net accuracy gains appear. A sympathetic reader cares because this technique offers a practical route to higher-fidelity quantum computations on noisy intermediate-scale hardware without requiring full fault-tolerant architectures.

Core claim

Quantum error detection can produce unbiased expectation values that exponentially converge to noiseless results as the code distance is increased. Detailed benchmarking on hardware and simulators with the repetition code and triangular color code, covering memory experiments and logical computations up to 74 physical qubits, together with pseudothreshold estimates, maps both the exponential sample overheads and the frontier at which increasing distance delivers net improvement.

What carries the argument

Pseudothreshold estimation applied to the repetition code and triangular color code, which locates the physical error rate below which larger distances reduce effective error after accounting for sampling and embedding costs.

If this is right

  • Logical computations on hardware can be performed with error detection up to tens of physical qubits while retaining exponential accuracy gains.
  • Unbiased expectation values from variational or other algorithms become obtainable by post-selection on detected errors.
  • The error-rate frontier for useful error detection is identified for present-day and near-future noise levels.
  • Memory and computation experiments become feasible at larger distances once embedding costs are managed.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same benchmarking approach could be repeated for other error-detecting codes to identify which perform best on specific hardware.
  • Error detection may serve as an intermediate step that improves results on near-term devices before full fault tolerance arrives.
  • Targeted tests on algorithms that rely on unbiased estimates would directly measure the practical speedup.
  • Hardware reductions in embedding overhead would shift the pseudothreshold favorably and widen the useful regime.

Load-bearing premise

The constant overhead of embedding the code and logical operations on hardware will not dominate the error reduction gained from increasing code distance.

What would settle it

An experiment on a fixed noisy device in which raising code distance from d to d+2 increases total runtime or effective output error rather than decreasing it.

Figures

Figures reproduced from arXiv: 2605.02861 by Ethan Egger, Hong-Ye Hu, Ryan LaRose, Vincent Russo, William J. Zeng, Yanis Le Fur.

Figure 1
Figure 1. Figure 1: FIG. 1. Wall clock time in seconds to compute codewords view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Repetition code memory experiment results on IBM Kyiv. Subplot (a) shows view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Logical Bell state preparation with the view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Results of preparing logical Bell states with the triangular color code on IBM Boston and measuring the expectation view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Histogram of results for preparing the logical Bell view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Individual physical qubit results for the repetition view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Methods for the repetition code Bell state experiment view at source ↗
read the original abstract

Quantum error detection can produce unbiased expectation values that exponentially converge to noiseless results as the code distance is increased. Despite this, its performance as an error mitigation technique is relatively understudied on quantum hardware because of its two main drawbacks: (i) the number of samples increases exponentially in the circuit depth/noise level, and (ii) the classical processing generally grows exponentially in the code distance, though exceptions exist. Additionally, the constant (but often large) overhead of embedding the code and logical operations on hardware can make accuracy worse instead of better. In this work, we seek to provide a clear picture of these opportunities and challenges for scaling quantum error detection on hardware. We do so by performing a detailed benchmarking study on real and simulated noisy quantum computers, using the repetition code and triangular color code for memory experiments and logical computations with up to $74$ physical qubits. In addition to these benchmarks, we estimate the pseudothreshold of codes to map the frontier of error detection on current and future quantum computers. Despite the challenges, our results show strong promise for scaling quantum error detection on hardware.

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.

Referee Report

0 major / 3 minor

Summary. The manuscript presents a benchmarking study of quantum error detection on quantum hardware and simulators, focusing on the repetition code and triangular color code. It reports memory experiments and logical computations using up to 74 physical qubits, estimates pseudothresholds to delineate viable regimes, and discusses the trade-offs of sampling overhead, classical post-processing, and embedding costs, ultimately concluding that the approach shows strong promise for scaling despite these challenges.

Significance. If the reported hardware benchmarks and pseudothreshold estimates hold, the work supplies concrete empirical data on when error detection yields net accuracy gains over physical-level computation. This is particularly useful for guiding near-term hardware design and error-mitigation strategies, as the study directly measures performance on real devices rather than relying solely on idealized models.

minor comments (3)
  1. [Abstract] Abstract: While the abstract summarizes the benchmarks and pseudothreshold estimates, it contains no numerical results, error bars, or key quantitative thresholds; including one or two headline numbers would allow readers to immediately assess the scale of the reported improvements.
  2. [Pseudothreshold estimation] Section on pseudothreshold estimation: The fitting procedure and data-selection criteria (e.g., range of noise strengths or exclusion of outlier runs) used to extract pseudothresholds should be stated explicitly so that the robustness of the 'strong promise' claim can be evaluated independently.
  3. [Figures] Figure captions and legends: Several plots comparing logical versus physical error rates would benefit from explicit labels for the code distances shown and a clear indication of which curves correspond to hardware versus simulation data.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of our manuscript and for recommending minor revision. The referee's assessment correctly identifies the core contributions: hardware benchmarks of repetition and triangular color codes for quantum error detection, pseudothreshold estimates, and discussion of sampling, post-processing, and embedding trade-offs. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper is an empirical benchmarking study that reports direct hardware and simulator measurements of error detection performance for repetition and triangular color codes (up to 74 qubits), including pseudothreshold estimates and overhead comparisons. No load-bearing mathematical derivations, first-principles predictions, or fitted-parameter extrapolations are present; the central claim of 'strong promise' rests on concrete experimental data rather than any chain that reduces to its own inputs by construction. Self-citations, if present, are not invoked to justify uniqueness theorems or ansatzes that would force the results.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The study rests on standard assumptions from quantum error correction literature about how noise affects encoded states and how pseudothresholds are defined; no new entities are introduced.

axioms (2)
  • domain assumption Embedding logical operations into the physical qubit layout incurs only constant overhead that does not grow with code distance.
    This is required for the claim that accuracy improves rather than worsens with larger codes.
  • domain assumption Standard depolarizing or Pauli noise models adequately capture the dominant errors on the tested hardware.
    Invoked implicitly when mapping experimental data to pseudothreshold estimates.

pith-pipeline@v0.9.0 · 5503 in / 1398 out tokens · 35598 ms · 2026-05-09T15:59:43.986912+00:00 · methodology

discussion (0)

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