Pith. sign in

REVIEW 4 major objections 5 minor 48 references

Implementation of Magic State Injection within Heavy-Hexagon Architecture

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Heavy-hexagon magic states should be injected with the ZXXZ-oriented XZZX code and down-triangle initialization.

desk verdict Competent applied simulation study; the orientation-dependent flag-qubit bias is plausible but the load-bearing error-propagation model is under-specified, so the recommendation should be treated as conditional until code, error bars, and post-selection logic are clarified. read the letter →

arxiv 2412.15751 v2 pith:RNHQODVA submitted 2024-12-20 quant-ph

Hansol Kim , Wonjae Choi , Younghun Kwon This is my paper · ORCID
classification quant-ph MSC 81P6881P70 PACS 03.67.Pp03.67.Lx
keywords magicstateinjectionheavy-hexagonarchitectureXZZXcodeflagqubitsbiasednoiselogicalerrorratefault-tolerantquantumcomputingcorrection
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 aims to determine how to inject magic states—specially prepared qubit states that let a quantum computer run non-Clifford gates—on hardware whose qubits connect like a heavy-hexagon graph rather than a square lattice. Because such hardware needs flag qubits to measure stabilizers, the paper asks whether the usual XZZX code, its rotated ZXXZ form, or the surface code performs best under realistic biased noise. It claims the ZXXZ form combined with a triangular qubit-initialization layout is the most suitable choice, and explains the advantage by a flag-qubit error-propagation mechanism that adds an effective noise bias beyond the one put into the error model. If true, this gives a concrete recipe for fault-tolerant non-Clifford gates on near-term heavy-hexagon processors and shows that code orientation should be chosen with the flag-qubit wiring, not just the lattice, in mind.

What carries the argument

The load-bearing object is the flag-qubit error-propagation mechanism in the stabilizer measurement network: a CNOT sends $X$ errors from control to target and $Z$ errors from target to control, so an $X$ error on a flag qubit lands on data qubits while a $Z$ error lands only on syndrome readout, and the symmetric CNOT pattern cancels errors picked up by other flag qubits. This mechanism creates an extra per-data-qubit bias whose sign depends on the stabilizer orientation: the XZZX orientation adds $X$-type errors, while the ZXXZ orientation adds $Z$-type errors. The second mechanism is the initialization geometry: the four region divisions (right square, down square, right triangle, down triangle) determine which single 'blind' data qubit cannot be error-checked, and the down-triangle layout places the blind qubit where fewer flag-qubit errors reach it.

What would settle it

Run the stabilizer-measurement circuit of Figure 5(b) with a single $X$ error inserted on each flag qubit in turn and check whether the flag-qubit measurements identify the source while the syndrome measurements stay silent; if any single flag $X$ error produces an ambiguous or wrong signature, the extra-bias mechanism is not what the hardware does. Then compare the logical error rates of the XZZX and ZXXZ orientations on a real heavy-hexagon device under strong $Z$ bias; the paper's recommendation fails if the ZXXZ orientation does not come out lower.

Watch

Extended reading notes

Core claim

The paper's central claim is that on a heavy-hexagon chip with flag qubits, the best way to inject a magic state is to encode it with the ZXXZ orientation of the XZZX code and initialize the surrounding data qubits with the down-triangle method. The reason is that flag qubits are not neutral: their $X$ errors propagate through the CNOT network into the data qubits, injecting an additional bias on top of the physical error model. The XZZX orientation adds extra $X$-type errors, weakening a $Z$ bias, while the ZXXZ orientation adds extra $Z$-type errors, reinforcing it; under the $Z$-biased noise typical of superconducting qubits, the ZXXZ orientation therefore has the lowest logical error rate. On a square lattice the two orientations are symmetric and equivalent, so the flag qubits are what break the symmetry.

Load-bearing premise

The paper's recommendation rests on the assumed error-propagation pattern through the flag-qubit network: an $X$ error on a flag qubit reaches the data, a $Z$ error reaches only the syndrome readout, and symmetric connections cancel secondary flag errors; if real hardware deviates from this pattern, the extra bias and the ZXXZ advantage disappear.

Editorial extensions

If this is right

  • On heavy-hexagon hardware with $Z$-biased noise, the ZXXZ orientation of the XZZX code should be used instead of the XZZX orientation for magic state injection.
  • Down-triangle initialization should be preferred at larger code distances, because triangle layouts keep error-detection regions compact and reduce the chain-error probability compared with square layouts.
  • Increasing the $Z$ bias lowers the heavy-hexagon logical error rate faster than on a square lattice because it suppresses the $X$ errors that flag qubits would otherwise inject into data qubits.
  • Errors introduced during the injection stage are not corrected by extending the code distance, so magic state distillation remains necessary even at low physical error rates.
  • The two XZZX orientations perform equivalently in the lattice structure, so any performance difference is a consequence of the flag-qubit wiring rather than of the code itself.

Reading between the lines

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

  • A direct testable extension: if the hardware's dominant noise were $X$-biased rather than $Z$-biased, the same flag-qubit mechanism should make the XZZX orientation the better choice; the paper does not run this case.
  • The conclusion is specific to the symmetric CNOT layout: on other low-degree graphs where flag-qubit connections are asymmetric, the extra bias could point the other way or become position-dependent, so the orientation choice would need to be derived per layout.
  • Because the extra bias is generated by the stabilizer measurement circuit itself, the effective noise seen by the data qubits differs from the nominal physical error model; this suggests error-model calibration should measure effective per-qubit bias after wiring, not just gate errors.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The manuscript reports a numerical study of magic state injection on the heavy-hexagon architecture that uses flag qubits. It compares the surface code and the two orientations of the XZZX code (labeled XZZX-type and ZXXZ-type) under depolarizing and Z-biased noise, four data-qubit initialization schemes, and distance extension from d1=3 to d2 up to 9. Simulations are performed with Stim and decoded with minimum-weight perfect matching. The main claim is that flag qubits introduce an extra bias whose sign depends on the stabilizer orientation, so that under Z-biased noise the ZXXZ-type XZZX code with the down-triangle initialization method is the most suitable magic state injection choice for the heavy-hexagon structure.

Significance. If the central claim is correct, the paper provides practically useful guidance for magic state preparation on IBM-style heavy-hexagon hardware, where the combination of limited connectivity and biased noise makes a direct transfer of square-lattice injection protocols non-optimal. The paper's strengths are its use of standard simulation tools (Stim, MWPM), a clearly specified biased error model, and a falsifiable, concrete recommendation. The main weakness is that the load-bearing flag-qubit error-propagation mechanism is asserted through schematic diagrams rather than fully documented circuits, and the numerical evidence is presented without statistical uncertainties. With additional circuit-level details, post-selection-aware analysis, and confidence intervals, the recommendation would be substantially more convincing.

major comments (4)
  1. [Section III.A, Figs. 5-6 and Appendix A] The paper explains the improved performance of the ZXXZ orientation by stating that X errors on flag qubits propagate to data qubits as extra X or Z errors and are identifiable through flag-qubit measurements. However, Appendix A describes a two-round post-selection procedure in which states with any detected error, including flag-triggered events, are discarded. If flag X errors are always identified and the corresponding states are discarded, they should not contribute to the logical error rate of accepted states. The manuscript needs to reconcile this tension: either the proposed extra-bias mechanism refers to raw, pre-post-selection error rates, or some flag X errors evade the flag measurement and survive post-selection. Please report accepted-state logical error rates together with flag-trigger statistics, and show explicitly how the extra-bias mechanism affects the post-selected logical error rate rather than only the rejected fraction of trials.
  2. [Section III.A and Fig. 5] The claimed error-propagation map is not independently verifiable from the text. The paper states that X errors on a flag qubit propagate to data qubits, Z errors propagate to syndrome qubits, and the symmetric CNOT layout causes propagated errors on other flag qubits to cancel, leaving the originating flag error identifiable. Figure 5 shows only schematic fragments of the stabilizer measurement circuit. The authors should provide the complete syndrome-extraction circuit for one surface-code stabilizer and one XZZX/ZXXZ stabilizer, including all flag-qubit CNOTs and measurement/reset placements, or provide the actual Stim circuit used in the simulations. Without this, the extra-bias asymmetry in Figure 6 and the resulting recommendation cannot be checked by an independent reader.
  3. [Section III.B and Figs. 8-10] No error bars or confidence intervals are reported for any logical error rate. The paper draws conclusions from differences between initialization methods and between XZZX and ZXXZ orientations, some of which appear small relative to the reported scales. With 1e7 samples, binomial uncertainties are not negligible for logical error rates in the 1e-4 to 1e-3 range, especially when comparing closely spaced curves. Please add confidence intervals or statistical significance tests, and state the number of accepted post-selected samples used for each reported point, since post-selection can substantially reduce the effective sample size.
  4. [Section III.C] The central explanation that the ZXXZ-type 'reinforces the Z bias' while the XZZX-type injects X bias remains qualitative. A quantitative validation would strengthen the claim: for example, estimate the effective bias per data qubit from the circuit-level propagation map or from simulation, and show that its sign and magnitude match the explanation; alternatively, vary the flag-qubit error rate independently of the data-qubit error rate and show that the XZZX/ZXXZ performance gap scales as predicted. Without such a test, the extra-bias mechanism is an interpretation of the observed trends rather than a demonstrated cause.
minor comments (5)
  1. [Section II.C, Eq. (1)] The text says that a Z error in all data qubits in the 'first row' corresponds to a logical Z error, but Eq. (1) initializes a column of d-1 qubits. Given the logical Z operator defined in Section II.A as a top-to-bottom chain, the first column appears to be intended. Please correct the terminology.
  2. [Section II.D, Eq. (4)] The expression P_Z = 2ηP_single/(2(η+1)) has a redundant factor of 2; simplifying to ηP_single/(η+1) would avoid confusion.
  3. [Section III.B and Fig. 7] The 'blind qubit' is not defined precisely. It is stated that one data qubit cannot have its errors detected, but it is not clear whether this is the magic-state qubit itself or a distinct boundary qubit, nor how its position is determined for each initialization method. Please provide an explicit definition.
  4. [Appendix A] The physical error rate is said to include two-qubit gate error and readout error rates, but the error model in Section II.D does not specify how readout errors are modeled. Please clarify the readout error model and how it enters the simulation.
  5. [Introduction] The phrase 'IBM's qubits typically exhibit T1 times longer than T2 times' is grammatically unclear; it should read that the T1 time is longer than the T2 time, or T1 >> T2.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the heavy-hexagon magic-state comparison is a simulation-based ranking, and prior self-citations are dependencies rather than assumed conclusions.

full rationale

The paper's central quantitative claims are logical error rates computed by Stim simulation of a fixed circuit family, not quantities fitted to the desired conclusion. The comparison of surface, XZZX, and ZXXZ codes and initialization methods is a ranking of measured simulation outputs; no parameter is fitted to make ZXXZ with down-triangle initialization win. The closest dependency is the heavy-hexagon flag-qubit code layout, which the text attributes to the authors' prior work: 'we previously introduced additional qubits known as flag qubits.' That is a reuse of an input code family, not a circular derivation: the paper's conclusion could in principle have gone the other way, and the flag-qubit error-propagation model in Section III.A is an asserted, checkable circuit-level claim whose failure would be a correctness risk, not a circularity. The Appendix A post-selection tension (flag-triggered events are discarded yet claimed to raise accepted-state error rates) is an internal consistency concern, not a circular reduction. Hence no circular step meets the quoted-equation standard.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The simulation rests on the authors' prior flag-qubit code construction and a hand-chosen error model. No fitting parameters are introduced to match data, but the single-qubit error ratio and the flag-qubit error propagation pattern are unvalidated assumptions that the central recommendation depends on.

free parameters (1)
  • single-qubit to two-qubit error ratio = 0.05 (1/20)
    Chosen by hand in Appendix A: 'the probability of single-qubit gate errors was assumed to be 1/20 of the two-qubit gate error probability.' This ratio affects all reported logical error rates but is not derived from hardware data.
assumptions (4)
  • standard math Stabilizer formalism and magic state injection theory
    Invoked throughout Section II.C to define how a physical magic state is encoded into a logical state via stabilizer measurements and post-selection.
  • domain assumption Heavy-hexagon code with flag qubits preserves the surface-code stabilizers and logical operators
    Section II.A states that 'by introducing flag qubits, the traditional surface code requirement for four-qubit connectivity is effectively adapted.' The code construction relies on the authors' prior work (refs [37], [39]) and is not re-derived here.
  • domain assumption Flag-qubit CNOT error propagation model
    Section III.A: 'X errors in the flag qubits act as additional errors in the data qubits, whereas Z errors contribute to measurement errors in the syndrome qubits.' Also assumes symmetric CNOTs cause propagated errors to cancel. This mechanism drives the main conclusions.
  • domain assumption Depolarizing and Z-biased Pauli error models with P_single = P_double/20
    Section II.D and Appendix A define the noise model. The bias model equations (4)-(6) are consistent at η=0.5 but are a modeling choice, not a measured hardware model.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Implementation of Magic State Injection within Heavy-Hexagon Architecture." pith.science (2026). https://pith.science/paper/RNHQODVA

@misc{pith2026241215751,
  author       = {Pith},
  title        = {Pith review of: Implementation of Magic State Injection within Heavy-Hexagon Architecture},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RNHQODVA}},
  note         = {Machine review of arXiv:2412.15751}
}
read the original abstract

The magic state injection process is a critical component of fault-tolerant quantum computing, and numerous studies have been conducted on this topic. Many existing studies have focused on square-lattice structures, where each qubit connects directly to four other qubits via two-qubit gates. However, hardware that does not follow a lattice structure, such as IBM's heavy-hexagon structure, is also under development. In these non-lattice structures, many quantum error correction (QEC) codes designed for lattice-based system cannot be directly applied. Adapting these codes often requires incorporating additional qubits, such as flag qubits. This alters the properties of the QEC code and introduces new variables into the magic state injection process. In this study, we implemented and compared the magic state injection process on a heavy-hexagon structure with flag qubits and a lattice structure without flag qubits. Additionally, we considered biased errors in superconducting hardware and investigated the impact of flag qubits under these conditions. Our analysis reveals that the inclusion of flag qubits introduces distinct characteristics into the magic state injection process, which are absent in systems without flag qubits. Based on these findings, we identify several critical considerations for performing magic state injection on heavy-hexagon systems incorporating flag qubits. Furthermore, we propose an optimized approach to maximize the efficacy of this process in such systems.

Figures

Figures reproduced from arXiv: 2412.15751 by the authors.

Figure 1
Figure 1. FIG. 1: Logical quantum circuit for implementing a Logical T gate: If the state [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2: (a) Surface code on the heavy-hexagon structure. (b) Surface code on the lattice structure. Data qubits are shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3: XZZX code on the heavy-hexagon structure. The XZZX code detects and corrects errors in logical state through a [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: FIG. 4: Types of stabilizer measurements. (a) Data qubits prepared as eigenstates of the stabilizer enable error detection [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5: (a) Error propagation circuit for the X stabilizer on in the surface code implemented in the heavy-hexagon structure. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6: Illustration of additional errors caused by X errors in flag qubits. For the surface code, additional X or Z errors occur [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: FIG. 7: Four types of qubit initialization. (a) Right triangle method, (b) down triangle method, (c) right square method, (d) [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: FIG. 8: Top: Comparison of logical error rates for heavy-hexagon and lattice structures with a bias of 0.5 and 100 without [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 10
Figure 10. Figure 10: FIG. 10: Logical error rates when extending a logical state prepared with a distance-3 error correction code to a distance-9 [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

48 extracted references · 43 canonical work pages

  1. [1]

    A., Baldwin, C

    Moses, S. A., Baldwin, C. H., Allman, M. S., Ancona, R., Ascarrunz, L., Barnes, C., ... & Pino, J. M. A race-track trapped-ion quantum processor. Physical Review X, 13(4), 041052.(2023)

    work page 2023

  2. [2]

    J., Bluvstein, D., Kalinowski, M., Ebadi, S., Manovitz, T., Zhou, H.,

    Evered, S. J., Bluvstein, D., Kalinowski, M., Ebadi, S., Manovitz, T., Zhou, H., ... & Lukin, M. D. High-fidelity parallel entangling gates on a neutral-atom quantum computer. Nature, 622(7982), 268-272.(2023)

    work page 2023

  3. [3]

    IBM Quantum Experience Devices

    IBM. IBM Quantum Experience Devices. https://quantum-computing.ibm.com/, 2024

    work page 2024

  4. [4]

    IonQ IonQ Quantum Computers https://ionq.com/quantum-systems/compare

    work page

  5. [5]

    Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer

    Shor, P.W. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Rev. 41, 303-332 (1999)

    work page 1999

  6. [6]

    Campbell, E. T. Early fault-tolerant simulations of the Hubbard model. Quantum Science and Technology, 7(1), 015007.(2021)

    work page 2021

  7. [7]

    D., Gidney, C., Berry, D

    Kivlichan, I. D., Gidney, C., Berry, D. W., Wiebe, N., McClean, J., Sun, W., ... & Babbush, R. Improved fault-tolerant quantum simulation of condensed-phase correlated electrons via trotterization. Quantum, 4, 296.(2020). 16

    work page 2020

  8. [8]

    Deutsch, D. (1985). Quantum theory, the Church-Turing principle and the universal quantum computer. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 400(1818), 97-117

    work page 1985

Show all 48 references
  1. [9]

    Grover, L. K. (1996, July). A fast quantum mechanical algorithm for database search. In Proceedings of the twenty-eighth annual ACM symposium on Theory of computing (pp. 212-219)

    1996

  2. [10]

    Bae J., Kwon Y. (2002). Generalized quantum search Hamiltonian Physical Review A 66 (1), 012314

    2002

  3. [11]

    Park S., Bae J. ,Kwon Y. (2007). Wavelet quantum search algorithm with partial information Chaos, Solitons & Fractals 32 (4), 1371-1374

    2007

  4. [12]

    Simon, D. R. (1997). On the power of quantum computation. SIAM journal on computing, 26(5), 1474-1483

    1997

  5. [13]

    Vazirani, U. (1998). On the power of quantum computation. Philosophical Transactions of the Royal Society of London. Series A. Mathematical, Physical and Engineering Sciences, 356(1743), 1759-1768

    1998

  6. [14]

    W., Hassidim, A., Lloyd, S

    Harrow, A. W., Hassidim, A., Lloyd, S. (2009). Quantum algorithm for linear systems of equations. Physical review letters, 103(15), 150502

    2009

  7. [15]

    Stabilizer codes and quantum error correction

    Gottesman, D. Stabilizer codes and quantum error correction. California Institute of Technology, 1997

    1997

  8. [16]

    Quantum error correction: an introductory guide

    Roffe, J. Quantum error correction: an introductory guide. Contemp. Phys. 60(3), 226 (2019)

    2019

  9. [17]

    G., Devitt, S., & Van Meter, R

    Horsman, D., Fowler, A. G., Devitt, S., & Van Meter, R. Surface code quantum computing by lattice surgery. New Journal of Physics, 14(12), 123011, 2012

    2012

  10. [18]

    J., & Ryan-Anderson, C

    Landahl, A. J., & Ryan-Anderson, C. Quantum computing by colorcode lattice surgery. arXiv:1407.5103, 2014

    2014 arXiv

  11. [19]

    G., Bertels, K., & Terhal, B

    Vuillot, C., Lao, L., Criger, B., Almudéver, C. G., Bertels, K., & Terhal, B. M. Code deformation and lattice surgery are gauge fixing. New Journal of Physics, 21(3):033028, 2019

    2019

  12. [20]

    Universal quantum computation with ideal Clifford gates and noisy ancillas

    Bravyi, S., and Kitaev, A. Universal quantum computation with ideal Clifford gates and noisy ancillas. Physical Review A 71.2:022316, 2005

    2005

  13. [21]

    Restrictions on transversal encoded quantum gate sets

    Eastin, B., and Knill, E. Restrictions on transversal encoded quantum gate sets. Physical review letters 102.11:110502, 2009

    2009

  14. [22]

    Zeng, B., Cross, A., & Chuang, I. L. Transversality versus universality for additive quantum codes. IEEE Transactions on Information Theory, 57(9):62726284, 2011

    2011

  15. [23]

    A magic state’s fidelity can be superior to the operations that created it

    Li, Y. A magic state’s fidelity can be superior to the operations that created it. New Journal of Physics 17.2:023037, 2015

    2015

  16. [24]

    S., Brown, B

    Singh, S., Darmawan, A. S., Brown, B. J., & Puri, S. High-fidelity magic state preparation with a biased-noise architecture. Physical Review A 105.5:052410, 2022

    2022

  17. [25]

    Magic state injection on the rotated surface code

    Lao, L., & Criger, B. Magic state injection on the rotated surface code. Proceedings of the 19th ACM International Conference on Computing Frontiers. 2022

    2022

  18. [26]

    Fault-tolerant quantum computation by Anyons

    Kitaev, A.Y. Fault-tolerant quantum computation by Anyons. Phys. 303, 2-30 (2003)

    2003

  19. [27]

    : Surface codes: towards practical large-scale quantum computation

    Fowler, A.G., Mariantoni, M., Martinis, J.M., Cleland, A.N. : Surface codes: towards practical large-scale quantum computation. Phys. Rev. A 86, 032324 (2012)

    2012

  20. [28]

    Correcting coherent errors with surface codes

    Bravyi, S., Englbrecht, M., König, R., & Peard, N. Correcting coherent errors with surface codes. NPJ Quantum Inf. 4(1), 1-6 (2018)

    2018

  21. [29]

    Nature 595.7867:383-387, 2021

    Google Quantum AI Exponential suppression of bit or phase errors with cyclic error correction. Nature 595.7867:383-387, 2021

    2021

  22. [30]

    Nature 614.7949:676-681, 2023

    Google Quantum AI Suppressing quantum errors by scaling a surface code logical qubit. Nature 614.7949:676-681, 2023

    2023

  23. [31]

    J., Srinivasan, S., Sundaresan, N., Bogorin, D

    Zhang, E. J., Srinivasan, S., Sundaresan, N., Bogorin, D. F., Martin, Y., Hertzberg, J. B., ... & Chow, J. M. High- performance superconducting quantum processors via laser annealing of transmon qubits. Science Advances 8.19:eabi6690, 2022

    2022

  24. [32]

    Fault-tolerant quantum computation with few qubits

    Chao, R., Reichardt, B.W. Fault-tolerant quantum computation with few qubits. NPJ Quantum Inf. 4, 42 (2018)

    2018

  25. [33]

    Flag fault-tolerant error correction for any stabilizer code

    Chao, R., Reichardt, B.W. Flag fault-tolerant error correction for any stabilizer code. PRX Quantum 1, 010302 (2020)

    2020

  26. [34]

    Fault-tolerant quantum error correction on near-term quantum processors using flag and bridge qubits

    Lao, L., Almudever, C.G. Fault-tolerant quantum error correction on near-term quantum processors using flag and bridge qubits. Phys. Rev. A 101, 032333 (2020)

    2020

  27. [35]

    J., Hertzberg, J

    Chamberland, C., Zhu, G., Yoder, T. J., Hertzberg, J. B., & Cross, A. W. Topological and subsystem codes on low-degree graphs with flag qubits. Physical Review X 10.1:011022, 2020

    2020

  28. [36]

    G., Ding, Y., & Xie, Y

    Wu, A., Li, G., Zhang, H., Guerreschi, G. G., Ding, Y., & Xie, Y. Mapping Surface Code to Superconducting Quantum Processors. arXiv preprint arXiv:2111.13729, 2021. Comparative study of quantum error correction strategies for the heavy-hexagonal lattice

    2021 arXiv

  29. [37]

    Design of Quantum error correcting code for biased error on heavy-hexagon structure

    Kim, Y., Kang, J., & Kwon, Y. Design of Quantum error correcting code for biased error on heavy-hexagon structure. arXiv preprint arXiv:2211.14038, 2022

    2022 arXiv

  30. [38]

    Comparative study of quantum error correction strategies for the heavy-hexagonal lattice

    Benito, C., López, E., Peropadre, B., Bermudez, A. Comparative study of quantum error correction strategies for the heavy-hexagonal lattice. arXiv preprint arXiv:2402.02185.(2024)

    2024 arXiv

  31. [39]

    Effectiveness of the syndrome extraction circuit with flag qubits on IBM quantum hardware

    Kim, Y., Kim, H., Kang, J., Choi, W., Kwon, Y. Effectiveness of the syndrome extraction circuit with flag qubits on IBM quantum hardware. arXiv preprint arXiv:2403.10217.(2024)

    2024

  32. [40]

    The XZZX surface code

    Bonilla Ataides, J.P., Tuckett, D.K., Bartlett, S.D., Flammia, S.T., Brown, B.J. The XZZX surface code. Nat. Commun. 12, 2172 (2021)

    2021

  33. [41]

    Practical quantum error correction with the XZZX code and Kerr-cat qubits

    Darmawan, A.S., Brown, B.J., Grimsmo, A.L., Tuckett, D.K., Puri, S. Practical quantum error correction with the XZZX code and Kerr-cat qubits. PRX Quantum 2, 030345 (2021)

    2021

  34. [42]

    magic state distillation with low overhead

    Bravyi, S., & Haah, J. magic state distillation with low overhead. Physical Review A 86.5:052329, 2012

    2012

  35. [43]

    Stim: A fast stabilizer circuit simulator

    Gidney, C. Stim: A fast stabilizer circuit simulator. Quantum. 5, 497, 2021

    2021

  36. [44]

    Sparse Blossom: correcting a million errors per core second with minimum-weight matching

    Higgott, O., & Gidney, C. Sparse Blossom: correcting a million errors per core second with minimum-weight matching. arXiv preprint arXiv:2303.15933, 2023. 17

    2023 arXiv

  37. [45]

    Paths, trees, and Flowers

    Edmonds, J. Paths, trees, and Flowers. Canadian Journal of Mathematics. 17, 449-467, 1965

    1965

  38. [46]

    Blossom V: A new implementation of a minimum cost perfect matching algorithm

    Kolmogorov, V. Blossom V: A new implementation of a minimum cost perfect matching algorithm. Mathematical Pro- gramming Computation 1, 43-67, 2009

    2009

  39. [47]

    G., Whiteside, A

    Fowler, A. G., Whiteside, A. C., & Hollenberg, L. C. Towards practical classical processing for the surface code. Physical review letters 108.18:180501, 2012

    2012

  40. [48]

    Fowler, A. G. Minimum weight perfect matching of fault-tolerant topological quantum error correction in average O(1) parallel time. Quantum Information and Computation. 15, 145-158, 2015. Acknowledgement We thank Younghun Kim for his contribution in early stage of this work. T...

    2015

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.