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arxiv: 2606.30606 · v1 · pith:MSUEFS4Ynew · submitted 2026-06-29 · 🪐 quant-ph

Repetition-code-based readout error detection and correction across hardware platforms and generations

Csaba Czab\'an , Orsolya K\'alm\'an , Sergey N. Filippov , Zolt\'an Zimbor\'as This is my paper

Pith reviewed 2026-06-30 05:47 UTC · model grok-4.3

classification 🪐 quant-ph
keywords readout errorrepetition codequantum error correctionsuperconducting qubitstrapped-ion qubitserror mitigationfidelity improvementcross-platform benchmark
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The pith

Repetition codes improve readout fidelity on both superconducting and trapped-ion processors across multiple hardware generations.

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

This paper tests a classical coding approach to readout errors by redundantly encoding each data qubit with ancilla qubits right before measurement, then decoding the bit string either by post-selection or majority vote. The protocol was run on successive generations of IBM superconducting devices and Quantinuum trapped-ion devices while varying code distance and layout. Both detection and correction raised fidelity on every platform even while unencoded baselines improved markedly over time. The payoff from extra redundancy proved platform-dependent because encoding gates add noise whose impact depends on the underlying gate error rate.

Core claim

Encoding data qubits with repetition codes immediately before measurement, followed by post-selection or majority-vote decoding, raises readout fidelity on every tested superconducting and trapped-ion processor. The gains hold across hardware generations despite large improvements in the unencoded baseline. On superconducting hardware the extra gates required for encoding quickly offset further redundancy, whereas on trapped-ion hardware the lower gate error rates allow larger code distances to remain advantageous.

What carries the argument

Repetition-code encoding of each data qubit with ancilla qubits before measurement, decoded by post-selection for error detection or majority voting for error correction.

If this is right

  • Both error detection by post-selection and error correction by majority vote improve fidelity on every device and generation examined.
  • The benefit of the method persists even as unencoded readout fidelity improves across successive hardware releases.
  • On superconducting processors the gate errors introduced by encoding limit the useful code distance.
  • On trapped-ion processors lower gate errors allow larger code distances to deliver additional gains.
  • The approach produces corrected individual measurement shots rather than only corrected aggregate statistics.

Where Pith is reading between the lines

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

  • Platform-specific optimization of code distance may be required rather than a single universal repetition-code choice.
  • The shot-level correction could be layered with other mitigation techniques for sampling-based algorithms where individual outcomes matter.
  • Hardware generations that further reduce two-qubit gate errors would likely expand the range of useful code distances on superconducting platforms.
  • The same encoding idea could be tested on other error sources that act only at the measurement stage.

Load-bearing premise

The fidelity gains are caused by the repetition-code encoding and decoding steps rather than by differences in calibration, crosstalk, or measurement settings between the encoded and unencoded runs.

What would settle it

An interleaved comparison on one device, with identical calibration and settings for encoded and unencoded shots, that shows no fidelity difference between the two would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.30606 by Csaba Czab\'an, Orsolya K\'alm\'an, Sergey N. Filippov, Zolt\'an Zimbor\'as.

Figure 1
Figure 1. Figure 1: FIG. 1. Circuit diagrams for implmenting the [3 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. The implementation of the chain (orange) and star [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. The cumulative fraction of qubits versus the esti [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. The cumulative fraction of qubits versus the esti [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. The cumulative fraction of qubits versus the estimated probability of detecting an error per qubit. Panels (a–c) use the [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Comparison of error detection efficiency with star [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Comparison of error detection efficiency with star and [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. The cumulative fraction of qubits versus the esti [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: FIG. 9. The cumulative fraction of qubits versus the esti [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
read the original abstract

Readout errors are one of the dominant sources of noise in current quantum processors, limiting both expectation-value estimation and sampling-based applications. Since they affect only the classical measurement outcomes, they can be addressed using classical coding techniques: immediately before measurement, each data qubit is redundantly encoded with ancilla qubits, and the resulting bit string is decoded either by post-selection or by majority voting. Unlike conventional readout error mitigation, which corrects only aggregate quantities such as expectation values, this approach operates on individual measurement shots and can therefore produce approximately corrected samples. We present a systematic cross-platform and cross-generation experimental evaluation of repetition-code readout error detection and correction. We benchmark the same protocol on IBM Heron r1-r3 superconducting processors and Quantinuum H1 and H2 trapped-ion processors while independently varying the code distance, hardware generation, and encoding layout. We find that both error detection and correction improve readout fidelity on every device and generation tested, even as the unencoded baseline improves substantially across successive hardware releases. At the same time, the value of additional redundancy depends strongly on the underlying hardware. On superconducting processors, the extra gate errors introduced by the encoding rapidly offset its benefits, whereas on trapped-ion processors the much lower gate error rates allow larger code distances to remain advantageous.

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

1 major / 2 minor

Summary. The manuscript presents a cross-platform experimental benchmark of repetition-code-based readout error detection (via post-selection) and correction (via majority voting) on IBM Heron r1-r3 superconducting processors and Quantinuum H1/H2 trapped-ion processors. The central empirical claim is that both techniques improve readout fidelity on every device and generation tested, even while unencoded baselines improve across hardware releases, with the utility of additional code distance being strongly hardware-dependent (rapidly offset by gate errors on superconductors but advantageous on ion traps).

Significance. If the attribution of gains to the encoding/decoding protocol holds, the work supplies concrete, hardware-specific guidance on when classical repetition coding is a net win for shot-level readout mitigation. The cross-generation and cross-platform scope is a strength, as is the direct comparison to unencoded baselines measured on the same devices. The results are relevant to near-term algorithms that rely on individual measurement samples rather than aggregate expectation values.

major comments (1)
  1. [Experimental methods / abstract] The experimental design (described in the abstract and implied methods) does not state that qubit calibrations, readout frequencies, pulse amplitudes, or crosstalk-suppression settings were held identical between encoded and unencoded runs on the same device. This is load-bearing for the central claim that observed fidelity gains arise from the repetition-code protocol rather than from undocumented changes in hardware state or measurement settings between the two classes of circuits.
minor comments (2)
  1. Error-bar details, number of shots per data point, and any post-selection rates or raw data tables are not referenced in the provided abstract; inclusion of these would strengthen auditability of the fidelity gains.
  2. The abstract states that layouts were varied but does not indicate whether the same physical qubits were used for encoded and baseline measurements; clarifying this would help rule out spatial variation as a confound.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for identifying this important point about experimental controls. We address the comment below and have revised the manuscript to include the requested clarification.

read point-by-point responses

  1. Referee: The experimental design (described in the abstract and implied methods) does not state that qubit calibrations, readout frequencies, pulse amplitudes, or crosstalk-suppression settings were held identical between encoded and unencoded runs on the same device. This is load-bearing for the central claim that observed fidelity gains arise from the repetition-code protocol rather than from undocumented changes in hardware state or measurement settings between the two classes of circuits.

    Authors: We agree that explicit confirmation of unchanged hardware settings is required to support the attribution of fidelity gains to the encoding protocol. The original manuscript did not include this statement. In the revised version we have added a new paragraph to the Experimental Methods section stating that, for each device, all qubit calibrations, readout frequencies, pulse amplitudes, and crosstalk-suppression settings were held fixed between the encoded and unencoded circuits. These parameters are set by the hardware provider’s automated calibration routines and were not altered between the two classes of experiments. This addition directly addresses the concern and strengthens the central claim. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental benchmarking with direct hardware comparisons

full rationale

The paper is a cross-platform experimental study that measures readout fidelity for repetition-code encoded circuits versus unencoded baselines on the same devices. No mathematical derivation, parameter fitting, or prediction is claimed; results are obtained by direct execution and post-processing on IBM and Quantinuum hardware. The central claim rests on empirical comparison rather than any self-referential definition or self-citation chain. No load-bearing step reduces to an input by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper is an experimental benchmarking study. No free parameters are fitted to produce the central claim, no new entities are postulated, and the axioms invoked are standard assumptions of quantum measurement and classical post-processing.

axioms (2)
  • standard math Measurement outcomes can be treated as classical bit strings after the final gates.
    Implicit in the use of repetition codes and majority voting on the measurement record.
  • domain assumption Gate errors introduced by the encoding circuit are the dominant additional noise source on superconducting hardware.
    Used to explain why larger code distances cease to help on IBM devices.

pith-pipeline@v0.9.1-grok · 5774 in / 1404 out tokens · 23732 ms · 2026-06-30T05:47:06.560193+00:00 · methodology

discussion (0)

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Reference graph

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