Pith. sign in

REVIEW 2 major objections 6 minor 89 references

Pauli-noise shortcuts can misjudge quantum error correction by 10x

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

T0 review · glm-5.2

2026-07-10 01:31 UTC pith:YQN5LQAY

load-bearing objection New simulation method (XPauli) for leakage and environmental noise in QEC, validated at small scale but with unverified approximation at the distances where the headline claims live the 2 major comments →

arxiv 2607.08767 v1 pith:YQN5LQAY submitted 2026-07-09 quant-ph

Plaquette: A hardware-aware design platform for fault-tolerant quantum computers

classification quant-ph PACS 03.67.Pp03.67.Lx03.65.Yz
keywords quantum error correctionfault-tolerant quantum computingstabilizer simulationleakage noisePauli twirlingthreshold estimationsurface codeopen quantum systems
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

Fault-tolerant quantum computers must be simulated under the noise that real hardware actually produces — leakage, coherent over-rotation, heating, scattering — not the simplified stochastic Pauli noise that fast simulators handle. This paper introduces a framework, called Plaquette, that takes a hardware error model specified once as a quantum channel (from Kraus operators, Lindblad dynamics, or experiment) and automatically compiles it into the representation each of four simulation methods needs. The central technical contribution is the XPauli sampler, which extends efficient stabilizer simulation to qubits that leak into higher energy levels or interact with environmental modes: it tracks those non-computational degrees of freedom as classical labels while retaining full stabilizer coherence for qubits that remain in the computational subspace. This hybrid representation keeps simulation cost polynomial rather than exponential. The paper validates XPauli and a near-Clifford sampler for coherent errors against exact full-state simulation on small codes, showing agreement within statistical uncertainty, while the standard Pauli-twirled approximation can underestimate logical error rates by over an order of magnitude and shift threshold estimates by roughly twenty percent. Three realistic hardware demonstrations — transmon leakage, neutral-atom scattering, and trapped-ion heating — show that the magnitude of the discrepancy depends on the platform and noise process, making the choice of simulation method a material design decision rather than a technical detail.

Core claim

The XPauli sampler rests on a specific structural assumption about quantum noise: that coherence between different leaked levels, environment sectors, or between leaked and computational subspaces can be discarded without losing the information that matters for logical error correction. Under this assumption, the state of a multi-qubit system with leakage and environmental coupling factors into a stabilizer state on the computational qubits tensored with classical labels for everything else. A generalized Pauli twirl converts any CPTP channel into a transition table over these labels plus conditional Pauli errors on computational qubits. Sampling then proceeds in two stages — first a random-

What carries the argument

The XPauli sampler's state representation: a stabilizer state on computational qubits, tensored with classical leakage-level labels and classical sector labels for environmental states. A generalized Pauli twirl converts hardware-derived Kraus channels into transition tables among these labels, with conditional Pauli errors on remaining computational qubits. The near-Clifford samplers decompose non-Clifford channels or unitaries as signed sums of Clifford elements, sampling Clifford circuits with quasiprobability weights that reconstruct the original channel in expectation.

Load-bearing premise

The XPauli sampler assumes that quantum coherence between different leaked levels, between leaked and computational subspaces, or between different environment sectors can be safely discarded — that these degrees of freedom behave classically. If leaked levels or environmental modes retain phase coherence that influences syndrome extraction, the generalized Pauli twirl underlying XPauli would introduce approximation errors whose magnitude the paper does not bound.

What would settle it

Construct a hardware noise model where leaked levels or environment sectors retain quantum coherence that affects logical error rates, and show that XPauli's logical error rate estimate diverges from full-state simulation beyond statistical uncertainty while a method that retains that coherence does not.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Hardware teams evaluating whether their device is below threshold should not rely on Pauli-twirled approximations alone — the resulting threshold and logical error rate estimates can be off by an order of magnitude or more, leading to misallocated engineering effort.
  • The XPauli sampler's classical-label approach could be extended to other non-Pauli noise structures beyond leakage and heating, such as crosstalk with classical memory or time-correlated noise, as long as the incoherence assumption holds for the additional degrees of freedom.
  • The separation between sampler representation and decoder model means decoders initialized from Pauli-twirled detector error models may be suboptimal for leakage or coherent noise — richer decoder models (correlated, leakage-aware, trajectory-conditioned) could improve correction performance beyond what current decoders achieve.
  • Threshold surfaces in multi-parameter hardware noise spaces, as demonstrated for neutral atoms, could become a standard design tool: rather than reporting a single threshold number, hardware characterization would map the full tradeoff surface among competing imperfections.

Where Pith is reading between the lines

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

  • If the incoherence assumption fails — for instance, if leaked levels retain phase coherence that interferes with subsequent gate operations or if leakage-mediated entanglement between qubits affects syndrome extraction — XPauli would introduce uncontrolled approximation errors whose magnitude is not bounded in the paper. The validity of the assumption likely depends on the specific hardware platfo
  • The observation that Pauli twirling can underestimate logical error rates by over an order of magnitude raises a concern for the broader QEC literature: many published threshold estimates based on Clifford-only simulation with Pauli-twirled noise may be systematically optimistic, and the direction of the bias (optimistic vs pessimistic) may depend on the specific noise structure in ways that are n
  • The framework's channel-first design could, in principle, be extended to simulate non-Markovian noise by embedding memory into explicit environment levels or sectors, but the paper notes this is limited to cases where the relevant memory can be represented within the circuit-level channel formalism — genuinely long-range temporal correlations may require fundamentally different simulation strategi

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

Summary. This paper presents Plaquette, a software framework for simulating fault-tolerant quantum error correction under realistic, non-Pauli hardware noise. The core technical contribution is the XPauli sampler, which extends stabilizer simulation by tracking leaked levels and environment sectors as classical labels while retaining coherence within the computational subspace. The framework also integrates near-Clifford samplers for coherent errors and full-state simulation for reference calculations. The paper validates XPauli and near-Clifford samplers against full-state simulation on small codes (distance-3 repetition code), then applies XPauli to three hardware-relevant scenarios: superconducting qubit leakage, neutral-atom intermediate-state scattering, and trapped-ion heating. The central quantitative finding is that Clifford-only Pauli-twirled simulations can underestimate logical error rates by over an order of magnitude and shift thresholds by approximately 20 percent relative to XPauli, which the authors argue is the more reliable approximation.

Significance. The paper addresses a genuine and important gap in FTQC design: the mismatch between the stochastic Pauli noise assumed by scalable stabilizer simulators and the richer noise structure of real hardware. The XPauli sampler is a well-motivated contribution that fills a practical niche between Clifford-only and full-state simulation. The channel-first design framework, which compiles a single physical error model into multiple sampler-specific representations, is a useful engineering contribution. The three hardware demonstrations are illustrative and span the major matter-qubit platforms. The key caveat, discussed below, is that the headline quantitative claims at large code distances rest on XPauli without independent full-state verification at those distances.

major comments (2)
  1. Section IIIC1, paragraph 2: The XPauli sampler's efficiency rests on the assumption that 'coherence between different leaked levels, sectors, or environment labels can be discarded.' This incoherence assumption is validated against full-state simulation only at distance 3 (Fig. 5b, Fig. 6a). At distance 9 (Fig. 6b), full-state is dropped and a 25–56× gap between XPauli and Stim is reported, but XPauli itself is not independently verified there. The threshold results at d=5–19 (Fig. 7), the 2.6× scattering-axis overestimate (Fig. 8), and the order-of-magnitude discrepancy at d=19 all depend on XPauli accuracy at distances where no full-state reference exists. The paper provides no bound on how the generalized Pauli twirl's approximation error scales with circuit depth or code distance. If leakage events accumulate coherently across rounds in a way the incoherence approximation misses, the
  2. Section IIID, paragraph on the trapped-ion model: The sector-dependent depolarization model (Eq. 22) uses a linear relation p_depol(n) = p_0 + kappa(2n+1) with p_0 = 1e-4 and kappa = 5e-3, described as 'a modelling relation based on Ref. [84].' This relation is the sole coupling between the vibrational sector and the computational qubit, and thus the entire trapped-ion demonstration rests on it. The paper should clarify whether this is a physically motivated mapping or a purely illustrative choice, and ideally provide a sensitivity analysis showing how the threshold estimate changes with different sector-to-rate assignments. Without this, it is unclear whether the trapped-ion results generalize beyond the specific functional form chosen.
minor comments (6)
  1. Fig. 5: The caption states '5,000 full-state shots and 200,000 shots for the other samplers,' but the main text (Section IVA, paragraph preceding Fig. 5) says '5,000 shots with the full-state sampler and 200,000 shots with the others.' Fig. 6a caption says '10^5 shots for Stim and XPauli and 10^4 shots for full-state,' which is inconsistent with the 5,000 figure. Please reconcile.
  2. Section IVB: The dimensionless parameters (omega=4.0, alpha=2.0, g=0.005, tau_CZ~444) are stated to be 'illustrative rather than tuned to a specific device.' It would help to note whether these are at least in a physically reasonable range for transmons, or whether they are purely abstract.
  3. Section IVD, Eq. (22): The statement 'p_0 = 10^{-4} and kappa = 5 x 10^{-3}, so that the depolarizing probability grows from about 0.5% in sector sigma=0 to 4.5% in sector sigma=4' appears to contain an arithmetic inconsistency: p_0 + kappa*(2*0+1) = 10^{-4} + 5e-3 = 5.1e-3 ~ 0.5%, which is consistent, but p_0 + kappa*(2*4+1) = 10^{-4} + 5e-3*9 = 4.51e-2 ~ 4.5%, which is also consistent. The values are fine; please just verify the intermediate sectors are as intended.
  4. Table II: The 'Near-Clifford sampling' entry lists four sub-methods but the distinction between 'general' and 'unitary' routes (channel-level vs. operator-level decomposition) could be clearer in the table. A brief note on when a user should choose one over the other would help.
  5. Section IIC: The threshold surface construction via barycentric scan directions is described, but the number of directions used (15 in Fig. 8) is mentioned only in the figure caption. Stating this in the main text would clarify the resolution of the surface.
  6. The paper uses 'Plaquette' both for the framework and the software suite. Occasional clarification that these refer to the same entity would help readers unfamiliar with the product.

Circularity Check

0 steps flagged

No significant circularity: Plaquette's central claims are validated against independent full-state benchmarks, not against their own outputs.

full rationale

The paper's central validation claim — that XPauli and near-Clifford samplers match full-state simulation — is tested against an independent reference method (exact state-vector propagation on CPU/GPU), not against XPauli's own output. The physical error models are derived from Hamiltonians and Lindblad operators (Eqs. 14–20) that are specified independently of the sampler representations. The threshold comparisons between XPauli and Clifford-only Stim (Figs. 7, 8) are comparisons of two different approximation methods applied to the same physical channel, not a self-referential loop. The generalized Pauli twirl that XPauli applies (Table III) is derived from the Kraus channel (Eq. 12) via a well-defined mathematical procedure, not fitted to the target output. The near-Clifford quasiprobability decompositions (Eqs. 6–7, Fig. 4) are obtained by solving a linear program or operator decomposition, not by fitting to logical error rates. No 'prediction' is equivalent to its input by construction. The only minor self-referential element is that the decoder DEM is constructed from a Pauli-twirled version of the same noisy circuit being sampled (Section IIID), but this is a standard, explicitly acknowledged design choice, not a circular derivation of the paper's claims. The paper's limitations (XPauli validated against full-state only at d=3, incoherence assumption unbounded at scale) are correctness concerns, not circularity: the claims do not reduce to their inputs by definition or by self-citation chain.

Axiom & Free-Parameter Ledger

6 free parameters · 4 axioms · 2 invented entities

The free parameters are physical model parameters chosen for illustration rather than fitted to experimental data. The key ad hoc axiom is the incoherence assumption underlying XPauli; the paper validates this on small codes but does not bound the approximation error analytically. The invented entities (XPauli sampler, channel-first framework) have independent falsifiable handles through comparison with full-state simulation.

free parameters (6)
  • Transmon Hamiltonian parameters (omega, alpha, g) = omega=4.0, alpha1=alpha2=2.0, g=0.005
    Stated as illustrative rather than tuned to a specific device (Section IVB, paragraph after Eq. 15).
  • Dissipation rate ratios (Gamma_heat, Gamma_cool relative to Gamma_deph) = Gamma_heat=0.02*gamma/tau_CZ, Gamma_cool=0.1*gamma/tau_CZ
    Chosen for illustrative purposes; values on actual hardware might differ (Section IVB).
  • Neutral-atom CZ drive parameters (Omega, delta, V0, Delta, xi, tau) = Omega=2.7382, delta=300, V0=200, Delta~0.38*Omega_eff, xi~3.90, tau~4.29/Omega_eff
    Fixed dimensionless units for the threshold surface scan; stated as illustrative (Section IVC).
  • Trapped-ion sector-to-depolarization mapping (p0, kappa) = p0=1e-4, kappa=5e-3
    Modeling relation based on Ref. 84, used to showcase sector-dependent depolarization (Section IVD, Eq. 22).
  • Trapped-ion thermalization parameters (Gamma_h range, nth) = Gamma_h in [0.0229, 0.0429], nth=1.0
    Scan range and equilibrium phonon number chosen for the heating threshold demonstration (Section IVD).
  • Near-Clifford decomposition robustness = R_op~1.10 for Eq. 9, R_op_tot~5.98 for the circuit
    Derived from the decomposition, not fitted, but determines the variance cost of the estimator (Section IVA).
axioms (4)
  • domain assumption Markovian (memoryless) dissipation governs the hardware noise processes
    The Lindblad master equation (Eq. 3) is used throughout to derive circuit-level channels. Non-Markovian noise is outside the present formalism (Section V).
  • ad hoc to paper Coherence between leaked levels, sectors, and environment labels can be discarded for the XPauli sampler
    This incoherence assumption (Section IIIC1) enables the classical-jump approach. If violated, the generalized Pauli twirl introduces uncontrolled approximation errors.
  • ad hoc to paper Pauli twirling of the noise model provides a sufficient decoder model (DEM) even when the sampler uses a richer representation
    The decoder is always initialized from a Pauli-twirled DEM (Section IIID). The paper acknowledges this is a current default, not a claim of optimality.
  • domain assumption Finite-size scaling with the form P_fail ~ F((eta-eta*)*d^(1/nu)) holds near threshold
    Used for threshold extraction (Eq. 8, Section IIIF). Standard in the QEC literature but involves interpolation assumptions for finite distances.
invented entities (2)
  • XPauli sampler independent evidence
    purpose: Extends stabilizer simulation to leakage and environment sectors by tracking non-computational degrees of freedom as classical labels
    Validated against full-state simulation on distance-3 codes (Fig. 5, Fig. 6a) with agreement within statistical uncertainty. The method is falsifiable: if coherence between leaked levels matters, XPauli would disagree with full-state results.
  • Channel-first design framework (Plaquette) independent evidence
    purpose: Specifies a physical error model once as CPTP channels and compiles it into multiple sampler representations
    The framework is demonstrated on three hardware platforms with different noise types. The automatic conversion between representations is verifiable by comparing outputs of different samplers on the same input channel.

pith-pipeline@v1.1.0-glm · 34750 in / 3637 out tokens · 297173 ms · 2026-07-10T01:31:46.691168+00:00 · methodology

0 comments
read the original abstract

Hardware teams building fault-tolerant quantum computers (FTQCs) must decide which imperfections to suppress, and that decision requires the logical performance of the architecture under the device's actual noise. Hardware noise often departs from the stochastic Pauli models used by scalable stabilizer simulators: superconducting transmons leak out of the computational subspace, neutral atoms scatter through intermediate states, trapped ions heat as their motional modes absorb phonons, and miscalibrated controls over-rotate coherently. We present Plaquette, a theoretical framework and software suite that computes the logical performance of fault-tolerant architectures directly from the physics of such imperfections. In Plaquette, a hardware error model is specified once, as Kraus operators, Hamiltonian-Lindblad dynamics, or an experimentally reconstructed quantum channel, and is compiled automatically into the exact or approximate representation required by each of four sampler classes: stabilizer sampling for Pauli noise, the new XPauli sampler for leakage and environment sectors, near-Clifford samplers for coherent errors, and full-state simulation for exact reference calculations. We validate the XPauli and near-Clifford samplers against full-state simulation, which they can match within statistical uncertainty while Pauli twirling can fall short depending on the error model. We demonstrate the framework on three error models: leakage in superconducting qubits, intermediate-state scattering in neutral atoms, and heating in trapped ions. The size of the discrepancy between Plaquette and Clifford-only simulations varies with platform and noise process, so reliable thresholds, error budgets, and overhead estimates require the most accurate simulation available. Plaquette provides a direct path from the open-system physics of a device to the logical performance of the FTQC built on it.

Figures

Figures reproduced from arXiv: 2607.08767 by Antal Sz\'ava, Carlos D\'iaz L\'opez, Davide Laureti, Ish Dhand, Ivan Ogloblin, Kshitij Kapoor, Marcello Massaro, Martin B. Plenio, Matteo Santandrea, Pranjal Nayak, Raphael Weber, Raul Conchello Vendrell, Shreya Prasanna Kumar, Trevor Vincent, Varun Seshadri.

Figure 1
Figure 1. Figure 1: FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Schematic of the [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. State representation underlying the XPauli sampler. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. The two quasiprobability near-Clifford samplers, illustrated on the coherent rotation [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. The XPauli and near-Clifford samplers reproduce the [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7. Logical error probability of rotated planar codes of [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Physical transmon- [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIG. 8. Threshold surfaces for the neutral-atom error model as [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 11
Figure 11. Figure 11: FIG. 11. Logical error rate of rotated planar-code memory [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

89 extracted references · 89 canonical work pages · 19 internal anchors

  1. [1]

    The XPauli sampler The XPauli sampler extends stabilizer simulation to systems whose qubits can leave the computational sub- space or carry additional classical environment labels. It is designed for noise in which coherence inside the computational subspace must be retained, but coherence between different leaked levels, sectors, or environment labels ca...

  2. [2]

    The near-Clifford samplers Plaquette’s near-Clifford samplers address a differ- ent limitation of stabilizer simulation. Coherent errors and native non-Clifford gates cannot be captured by the 9 Rz(0.1π) General Near-Clifford Channel-level decomposition Decomposition α1 I·ρ·I†+α2 S·ρ·S†+α3 Z·ρ·Z† Coefficients α1 = 0.821α2 = 0.309α3 =−0.130 Robustness Rch ...

  3. [3]

    = +1 I·ρ·S† |c1c∗ 2| R2op phase(c1c∗ 2) S·ρ·I† |c2c∗ 1| R2op phase(c2c∗ 1) S·ρ·S† |c2c∗ 2| R2op phase(c2c∗

  4. [4]

    = +1 Pfail =R2op·mean(wjkOjk) FIG. 4. The two quasiprobability near-Clifford samplers, illustrated on the coherent rotationRz(0.1π). (Left) The general sampler decomposes thechannelas E = ∑ kαkΦ k, whereΦ k denotes a Clifford channel; each shot draws one channel with probability|αk|/Rch and carries the sign ofαk as its weight. (Right) The unitary sampler ...

  5. [5]

    They do not reduce the channel to a Pauli, XPauli, or near- Clifford representation; instead, they propagate the state under the Kraus channels specified by the error model

    Full-state sampling on CPU and GPU The full-state samplers are the reference methods. They do not reduce the channel to a Pauli, XPauli, or near- Clifford representation; instead, they propagate the state under the Kraus channels specified by the error model. Their memory and runtime costs are exponential in the number of levels included in the full-state...

  6. [6]

    Aharonov and M

    D. Aharonov and M. Ben-Or, Fault-tolerant quantum computation with constant error rate, SIAM J. Comput. 38, 1207 (2008). 20

  7. [7]

    Knill, R

    E. Knill, R. Laflamme, and W. H. Zurek, Resilient quan- tum computation, Science279, 342 (1998)

  8. [8]

    Acharya, I

    Google Quantum AI, R. Acharya, I. Aleiner, R. Allen, T. I. Andersen, M. Ansmann, F. Arute, K. Arya, A. As- faw, J. Atalaya, R. Babbush, D. Bacon, J. C. Bardin, J. Basso, A. Bengtsson, S. Boixo, G. Bortoli, A. Bourassa, J. Bovaird, L. Brill, M. Broughton, B. B. Buckley, D. A. Buell, T. Burger, B. Burkett, N. Bushnell, Y. Chen, Z. Chen, B. Chiaro, J. Cogan,...

  9. [9]

    The Heisenberg Representation of Quantum Computers

    D. Gottesman, The heisenberg representation of quantum computers, arXiv:quant-ph/9807006 10.48550/arXiv.quant-ph/9807006 (1998), arXiv: quant-ph/9807006

  10. [10]

    Aaronson and D

    S. Aaronson and D. Gottesman, Improved simulation of stabilizer circuits, Phys. Rev. A70, 052328 (2004), publisher: American Physical Society

  11. [11]

    Gidney, Stim: a fast stabilizer circuit simulator, Quan- tum5, 497 (2021)

    C. Gidney, Stim: a fast stabilizer circuit simulator, Quan- tum5, 497 (2021)

  12. [12]

    Motzoi, J

    F. Motzoi, J. M. Gambetta, P. Rebentrost, and F. K. Wil- helm, Simple pulses for elimination of leakage in weakly nonlinear qubits, Phys. Rev. Lett.103, 110501 (2009)

  13. [13]

    McEwen, D

    M. McEwen, D. Kafri, Z. Chen, J. Atalaya, K. J. Satzinger, C. Quintana, P. V. Klimov, D. Sank, C. Gidney, A. G. Fowler, F. Arute, K. Arya, B. Buckley, B. Bur- kett, N. Bushnell, B. Chiaro, R. Collins, S. Demura, A. Dunsworth, C. Erickson, B. Foxen, M. Giustina, T. Huang, S. Hong, E. Jeffrey, S. Kim, K. Kechedzhi, F. Kostritsa, P. Laptev, A. Megrant, X. Mi...

  14. [14]

    S. J. Evered, D. Bluvstein, M. Kalinowski, S. Ebadi, T. Manovitz, H. Zhou, S. H. Li, A. A. Geim, T. T. Wang, N. Maskara, H. Levine, G. Semeghini, M. Greiner, V. Vuletić, and M. D. Lukin, High-fidelity parallel entan- gling gates on a neutral-atom quantum computer, Nature 622, 268 (2023)

  15. [15]

    Saffman, T

    M. Saffman, T. G. Walker, and K. Mølmer, Quantum information with rydberg atoms, Rev. Mod. Phys.82, 2313 (2010)

  16. [16]

    Brownnutt, M

    M. Brownnutt, M. Kumph, P. Rabl, and R. Blatt, Ion- trap measurements of electric-field noise near surfaces, Rev. Mod. Phys.87, 1419 (2015)

  17. [17]

    J. D. Thompson, T. G. Tiecke, A. S. Zibrov, V. Vuletić, and M. D. Lukin, Coherence and raman sideband cooling of a single atom in an optical tweezer, Phys. Rev. Lett. 110, 133001 (2013)

  18. [18]

    C. H. Yang, A. Rossi, R. Ruskov, N. S. Lai, F. A. Mo- hiyaddin, S. Lee, C. Tahan, G. Klimeck, A. Morello, and A. S. Dzurak, Spin-valley lifetimes in a silicon quantum dot with tunable valley splitting, Nat. Commun.4, 2069 (2013)

  19. [19]

    Buterakos and S

    D. Buterakos and S. Das Sarma, Spin-valley qubit dy- namics in exchange-coupled silicon quantum dots, PRX Quantum2, 040358 (2021)

  20. [20]

    Bravyi, M

    S. Bravyi, M. Englbrecht, R. König, and N. Peard, Cor- recting coherent errors with surface codes, npj Quantum Inf.4, 55 (2018)

  21. [21]

    J. J. Wallman and J. Emerson, Noise tailoring for scalable quantum computation via randomized compiling, Phys. Rev. A94, 052325 (2016)

  22. [22]

    M. R. Geller and Z. Zhou, Efficient error models for fault-tolerant architectures and the pauli twirling approx- imation, Phys. Rev. A88, 012314 (2013)

  23. [23]

    C. T. Hann, K. Noh, H. Putterman, M. H. Matheny, J. K. Iverson, M. T. Fang, C. Chamberland, O. Painter, and F. G. Brandão, Hybrid cat-transmon architecture for scalable, hardware-efficient quantum error correction, PRX Quantum6, 030305 (2025)

  24. [24]

    Ryan-Anderson, J

    C. Ryan-Anderson, J. G. Bohnet, K. Lee, D. Gresh, A. Hankin, J. P. Gaebler, D. Francois, A. Chernoguzov, D. Lucchetti, N. C. Brown, T. M. Gatterman, S. K. Halit, K. Gilmore, J. A. Gerber, B. Neyenhuis, D. Hayes, and R. P. Stutz, Realization of real-time fault-tolerant quan- tum error correction, Phys. Rev. X11, 041058 (2021)

  25. [25]

    J. D. Chadwick, G. G. Guerreschi, F. Luthi, M. T. Mądzik, F. A. Mohiyaddin, P. Prabhu, A. T. Schmitz, A. Litteken, S. Premaratne, N. C. Bishop, A. Y. Matsuura, and J. S. Clarke, Short two-qubit pulse sequences for exchange-only spin qubits in two-dimensional layouts, Phys. Rev. A111, 052616 (2025)

  26. [26]

    E. Tham, M. L. Goldman, S. Debnath, A. N. Pa- tel, J. Saraladevi, J. Nguyen, E. Nielsen, N. Pisenti, K. Wright, J. Gamble, and N. Delfosse, Breakeven demon- stration of quantum low-density parity-check codes (2026), arXiv:2606.06455 [quant-ph]

  27. [27]

    F.-M. L. Régent, C. Berdou, Z. Leghtas, J. Guillaud, and M. Mirrahimi, High-performance repetition cat code using fast noisy operations, Quantum7, 1198 (2023)

  28. [28]

    Hopfmueller, M

    F. Hopfmueller, M. Tremblay, P. St-Jean, B. Royer, and M.-A. Lemonde, Bosonic pauli+: Efficient simulation of concatenated gottesman-kitaev-preskill codes, Quantum 8, 1539 (2024). 21

  29. [29]

    A. G. Fowler, Coping with qubit leakage in topological codes, Phys. Rev. A88, 042308 (2013)

  30. [30]

    Leakage Suppression in the Toric Code

    M. Suchara, A. W. Cross, and J. M. Gambetta, Leakage suppression in the toric code, arXiv preprint arXiv:1410.8562 10.48550/arXiv.1410.8562 (2014), arXiv:1410.8562 [quant-ph]

  31. [31]

    QC Design Team, (2026), manuscript in preparation

  32. [32]

    M. A. Nielsen and I. L. Chuang, Quantum computation and quantum information (2010), iSBN: 9780511976667 Publisher: Cambridge University Press

  33. [33]

    A dynamical interpretation of the Pauli Twirling Approximation and Quantum Error Correction

    A. Katabarwa, A dynamical interpretation of the pauli twirling approximation and quantum error correction (2017), arXiv:1701.03708 [quant-ph]

  34. [34]

    A. R. Calderbank and P. W. Shor, Good quantum error- correcting codes exist, Phys. Rev. A54, 1098 (1996)

  35. [35]

    A. M. Steane, Error correcting codes in quantum theory, Phys. Rev. Lett.77, 793 (1996)

  36. [36]

    Subsystem surface codes with three-qubit check operators

    S. Bravyi, G. Duclos-Cianci, D. Poulin, and M. Suchara, Subsystem surface codes with three-qubit check operators, arXiv preprint arXiv:1207.1443 10.48550/arXiv.1207.1443 (2012)

  37. [37]

    Higgott and N

    O. Higgott and N. P. Breuckmann, Subsystem codes with high thresholds by gauge fixing and reduced qubit over- head, Phys. Rev. X11, 031039 (2021)

  38. [38]

    Topological Quantum Distillation

    H. Bombin and M. A. Martin-Delgado, Topological quan- tum distillation, Phys. Rev. Lett.97, 180501 (2006), arXiv:quant-ph/0605138

  39. [39]

    S.-H. Lee, A. Li, and S. D. Bartlett, Color code decoder with improved scaling for correcting circuit-level noise, Quantum9, 1609 (2025)

  40. [40]

    S. B. Bravyi and A. Y. Kitaev, Quantum codes on a lattice with boundary (1998), arXiv:quant-ph/9811052

  41. [41]

    Horsman, A

    D. Horsman, A. G. Fowler, S. Devitt, and R. V. Meter, Surface code quantum computing by lattice surgery, New J. Phys.14, 123011 (2012), publisher: IOP Publishing

  42. [42]

    P. W. Shor, Scheme for reducing decoherence in quan- tum computer memory, Phys. Rev. A52, R2493 (1995), publisher: American Physical Society

  43. [43]

    Laflamme, C

    R. Laflamme, C. Miquel, J. P. Paz, and W. H. Zurek, Perfect quantum error correcting code, Phys. Rev. Lett. 77, 198 (1996), publisher: American Physical Society

  44. [44]

    N. P. Breuckmann and J. N. Eberhardt, Quantum low- density parity-check codes, PRX Quantum2, 040101 (2021)

  45. [45]

    A. Y. Kitaev, Quantum computations: algorithms and error correction, Russ. Math. Surv.52, 1191 (1997), pub- lisher: IOP Publishing

  46. [46]

    Tillich and G

    J.-P. Tillich and G. Zemor, Quantum ldpc codes with positive rate and minimum distance proportional to the square root of the blocklength, IEEE Trans. Inf. Theory 60, 1193 (2013)

  47. [47]

    High-threshold and low-overhead fault-tolerant quantum memory

    S. Bravyi, A. W. Cross, J. M. Gambetta, D. Maslov, P. Rall, and T. J. Yoder, High-threshold and low-overhead fault-tolerant quantum memory, Nature627, 778 (2024), arXiv:2308.07915 [quant-ph]

  48. [48]

    Panteleev and G

    P. Panteleev and G. Kalachev, Degenerate quantum ldpc codes with good finite length performance, Quantum5, 585 (2021)

  49. [49]

    Panteleev and G

    P. Panteleev and G. Kalachev, Quantum ldpc codes with almost linear minimum distance, IEEE Trans. Inf. Theory 68, 213 (2021)

  50. [50]

    A Topologically Fault-Tolerant Quantum Computer with Four Dimensional Geometric Codes

    D. Aasen, M. B. Hastings, V. Kliuchnikov, J. M. Bello- Rivas, A.Paetznick, R.Chao, B.W.Reichardt, M.Zanner, M. P. da Silva, Z. Wang, and K. M. Svore, A topologically fault-tolerant quantum computer with four dimensional geometric codes (2025), arXiv:2506.15130 [quant-ph]

  51. [51]

    Gidney, M

    C. Gidney, M. Newman, A. Fowler, and M. Broughton, A fault-tolerant honeycomb memory, Quantum5, 605 (2021)

  52. [52]

    R. S. Bennink, E. M. Ferragut, T. S. Humble, J. A. Laska, J. J. Nutaro, M. G. Pleszkoch, and R. C. Pooser, Unbiased simulation of near-clifford quantum circuits, Phys. Rev. A95, 062337 (2017)

  53. [53]

    Hakkaku, K

    S. Hakkaku, K. Mitarai, and K. Fujii, Sampling-based quasiprobability simulation for fault-tolerant quantum error correction on the surface codes under coherent noise, Phys. Rev. Res.3, 043130 (2021)

  54. [54]

    Simulation of quantum circuits by low-rank stabilizer decompositions

    S. Bravyi, D. Browne, P. Calpin, E. Campbell, D. Gosset, and M. Howard, Simulation of quantum circuits by low- rank stabilizer decompositions, Quantum3, 181 (2019), arXiv:1808.00128 [quant-ph]

  55. [55]

    LeBlond, P

    T. LeBlond, P. Groszkowski, J. G. Lietz, C. M. Seck, and R. S. Bennink, Logical error rates for the surface code under a mixed coherent and stochastic circuit-level noise model inspired by trapped ions, Phys. Rev. Res.7, 043184 (2025)

  56. [56]

    Haenel, X

    R. Haenel, X. Luo, and C. Zhao, Tsim: Fast universal simulator for quantum error correction, arXiv preprint arXiv:2604.01059 10.48550/arXiv.2604.01059 (2026)

  57. [57]

    B. A. Chase and F. Labib, Clifft: Fast exact simula- tion of near-clifford quantum circuits, arXiv preprint arXiv:2604.27058 10.48550/arXiv.2604.27058 (2026)

  58. [58]

    Marshall and D

    J. Marshall and D. Kafri, Incoherent approximation of leakage in quantum error correction, Phys. Rev. Appl.23, 054025 (2025)

  59. [59]

    Efficient Simulation of Leakage Errors in Quantum Error Correcting Codes Using Tensor Network Methods

    H. Manabe, Y. Suzuki, and A. S. Darmawan, Efficient sim- ulation of leakage errors in quantum error correcting codes using tensor network methods (2025), arXiv:2308.08186 [quant-ph]

  60. [60]

    Camps, O

    J. Camps, O. Crawford, G. P. Gehér, A. V. Gramolin, M. P. Stafford, and M. L. Turner, Leakage mobility in superconducting qubits as a leakage reduction unit, Phys. Scr.101, 115107 (2026)

  61. [61]

    Efficient simulation of Clifford circuits with small Markovian errors

    A. Miller, C. Ostrove, J. Hines, R. Blume-Kohout, K. Young, and T. Proctor, Efficient simulation of clifford circuits with small markovian errors, arXiv preprint arXiv:2504.15128 10.48550/arXiv.2504.15128 (2025), arXiv:2504.15128 [quant-ph]

  62. [62]

    Developers, Cirq (2024), see full list of authors on Github

    C. Developers, Cirq (2024), see full list of authors on Github

  63. [63]

    Bayraktar, A

    H. Bayraktar, A. Charara, D. Clark, S. Cohen, T. Costa, Y.-L. L. Fang, Y. Gao, J. Guan, J. Gunnels, A. Haidar, A. Hehn, M. Hohnerbach, M. Jones, T. Lubowe, D. Lyakh, S. Morino, P. Springer, S. Stanwyck, I. Terentyev, S. Varadhan, J. Wong, and T. Yamaguchi, cuquantum sdk: A high-performance library for accelerating quantum science, in2023 IEEE Internationa...

  64. [64]

    Estimating detector error models from syndrome data

    R. Blume-Kohout and K. Young, Estimating detector er- ror models from syndrome data (2025), arXiv:2504.14643 [quant-ph]

  65. [65]

    Logical error estimation from syndrome data of surface-code experiments

    E. Takou, C. Benito, A. Vezvaee, D. A. Lidar, and K. R. Brown, Logical error estimation from syndrome data of surface-code experiments (2026), arXiv:2606.11496 [quant- ph]

  66. [66]

    Higgott, Pymatching: A python package for decoding quantum codes with minimum-weight perfect matching, 22 ACM Trans

    O. Higgott, Pymatching: A python package for decoding quantum codes with minimum-weight perfect matching, 22 ACM Trans. Quantum Comput.3, 1 (2022)

  67. [67]

    Fusion Blossom: Fast MWPM Decoders for QEC

    Y. Wu and L. Zhong, Fusion blossom: Fast mwpm de- coders for qec, in2023 IEEE International Conference on Quantum Computing and Engineering (QCE)(2023) arXiv:2305.08307 [quant-ph]

  68. [68]

    New circuits and an open source decoder for the color code

    C. Gidney and C. Jones, New circuits and an open source decoder for the color code (2023), arXiv:2312.08813 [quant-ph]

  69. [69]

    Roffe, D

    J. Roffe, D. R. White, S. Burton, and E. Campbell, De- coding across the quantum low-density parity-check code landscape, Phys. Rev. Res.2, 043423 (2020)

  70. [70]

    Hillmann, L

    T. Hillmann, L. Berent, A. O. Quintavalle, J. Eisert, R. Wille, and J. Roffe, Localized statistics decoding for quantum low-density parity-check codes, arXiv preprint arXiv:2406.18655 10.48550/arXiv.2406.18655 (2024)

  71. [71]

    Improved belief propagation is sufficient for real-time decoding of quantum memory

    T. Müller, T. Alexander, M. E. Beverland, M. Bühler, B. R. Johnson, T. Maurer, and D. Vandeth, Improved belief propagation is sufficient for real-time decoding of quantum memory (2025), arXiv:2506.01779 [quant-ph]

  72. [72]

    O.Higgott, T.C.Bohdanowicz, A.Kubica, S.T.Flammia, and E. T. Campbell, Improved decoding of circuit noise and fragile boundaries of tailored surface codes (2023)

  73. [73]

    L. A. Beni, O. Higgott, and N. Shutty, Tesseract: A search-based decoder for quantum error correction (2025), arXiv:2503.10988 [quant-ph]

  74. [74]

    S. Gu, Y. Vaknin, A. Retzker, and A. Kubica, Optimizing quantum error-correction protocols with erasure qubits, PRX Quantum6, 040354 (2025), Figs. 3b and 4d

  75. [75]

    Yu, Z.-H

    C.-C. Yu, Z.-H. Chen, Y.-H. Deng, C.-Y. Lu, M.-C. Chen, and J.-W. Pan, Taming rydberg decay with measurement- based quantum computation, Phys. Rev. Lett.136, 160601 (2026), Fig. 4a

  76. [76]

    Baranes, M

    G. Baranes, M. Cain, J. P. B. Ataides, D. Bluvstein, J. Sin- clair, V. Vuletić, H. Zhou, and M. D. Lukin, Leveraging qubit loss detection in fault-tolerant quantum algorithms, Phys. Rev. X16, 011002 (2026)

  77. [77]

    A. M. Stephens, Fault-tolerant thresholds for quantum error correction with the surface code, Phys. Rev. A89, 022321 (2014)

  78. [78]

    C. Wang, J. Harrington, and J. Preskill, Confinement- higgs transition in a disordered gauge theory and the accuracy threshold for quantum memory, Ann. Phys.303, 31 (2003)

  79. [79]

    D. Wang, A. Fowler, A. Stephens, and L. Hollenberg, Threshold error rates for the toric and planar codes, Quan- tum Inf. Comput.10, 456 (2010)

  80. [80]

    H. G. Katzgraber, H. Bombin, and M. A. Martin-Delgado, Error threshold for color codes and random three-body ising models, Phys. Rev. Lett.103, 090501 (2009)

Showing first 80 references.