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arxiv: 2304.04743 · v1 · pith:GW7RLM4Hnew · submitted 2023-04-10 · 🪐 quant-ph · cs.IT· math.IT

Improved Logical Error Rate via List Decoding of Quantum Polar Codes

Pith reviewed 2026-05-24 08:56 UTC · model grok-4.3

classification 🪐 quant-ph cs.ITmath.IT
keywords quantum polar codeslist decodingsuccessive cancellationlogical error ratestabilizer codespolarization weightdegenerate errorsequivalence classes
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The pith

Class-oriented list decoding lowers the logical error rate of quantum polar codes compared with exact-pattern decoding.

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

The paper adapts the classical successive cancellation list decoder to quantum polar codes built with the polarization weight method, which requires no entanglement assistance. The decoder can be run in two modes: one that recovers the exact error pattern and one that approximates the probability of each equivalence class of errors before selecting the most likely class. The class-oriented mode produces a noticeable drop in logical error rate while preserving the original decoder complexity of order L N log N. The same constructions compete in performance with surface codes and low-density parity-check codes of comparable size and rate.

Core claim

When the successive cancellation list decoder is applied to quantum polar codes constructed via the polarization weight method, using it to approximate and select the most likely error equivalence class yields a lower logical error rate than using it to identify the precise error pattern. This holds because low-weight errors already give a reasonable approximation to the class probabilities. Both modes retain the classical complexity scaling and the polarization-weight construction avoids any need for auxiliary entanglement.

What carries the argument

The class-oriented successive cancellation list decoder (SCL-C), which estimates probabilities over error equivalence classes rather than individual patterns.

If this is right

  • SCL-E decoding already matches the performance of surface codes and LDPC codes of similar length and rate.
  • SCL-C decoding improves logical error rate over SCL-E at identical complexity O(L N log N).
  • The list decoder supplies information about the weight distribution of the code and the impact of degenerate errors.
  • Contributions from only the low-weight errors suffice for a useful approximation to class probabilities.

Where Pith is reading between the lines

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

  • The same class-oriented approach could be tested on other quantum code families whose stabilizers admit efficient list decoding.
  • Code design that explicitly optimizes the low-weight part of the weight enumerator might further amplify the observed gain.
  • Hybrid decoders that switch between exact and class modes depending on list size could be benchmarked on the same codes.

Load-bearing premise

The polarization weight construction yields valid quantum stabilizer codes that achieve the stated rates and distances without entanglement assistance.

What would settle it

A direct check on a depolarizing channel showing that the logical error rate under SCL-C is no better than under SCL-E for the same list size, or that the constructed codes have distance below the claimed value.

Figures

Figures reproduced from arXiv: 2304.04743 by Anqi Gong, Joseph M. Renes.

Figure 1
Figure 1. Figure 1: The position of the two information bits (red) of the families of [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: shows the SCL-E decoder accuracy for a range of code sizes and noise parameters. One can readily see the even/odd n “ log2 N distinction, just as with the code distance from [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Performance of the SCL-E (solid) and SCL-C (dotted) decoder on the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Impact of list size on the ¹512, 2, 16º PW-QPC. Only diminishing gain for SCL-E can be obtained using a list size larger than 16. While the improvement of SCL-C continues to grow, though tiny, at larger p. The codes also compare favorably to constant-rate QLDPC codes constructed by taking the hypergraph product of randomly generated classical LDPC codes and decoded via the BP+OSD-CS method, as reported in … view at source ↗
Figure 5
Figure 5. Figure 5: Logical X error rate of higher-rate PW-QPC codes. The ¹1024, 32, 16º and ¹64, 2, 8º codes have the same rate, 1{32, but the former has a lower logical error rate than the latter the physical error rate is low. A slight improvement of SCL-C over SCL-E still can be seen in the blue curves. We reduced the β by 0.02 in the orange curve so as to maintain the distance and to form a fair comparison to the rate 1{… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of the ¹N, 1º Q1-QPC and PW-QPC. Logical X-error rate P X L (dashed), logical Z-error rate P Z L (dotted), combined logical error rate PL “ 1´ p1´ P Z L qp1´ P X L q (solid). Plots 1 and 2 n even. Plots 3 and 4 n odd. List size 1 (SC decoding) is already the MWD for the Q1-QPC. The logical error rates of the PW plots (right column) are the results of the SCL-E decoder and the list sizes used for… view at source ↗
Figure 7
Figure 7. Figure 7: Logical error rate of the SCL-E (solid) and the SCL-C (dotted) decoder for the [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
read the original abstract

The successive cancellation list decoder (SCL) is an efficient decoder for classical polar codes with low decoding error, approximating the maximum likelihood decoder (MLD) for small list sizes. Here we adapt the SCL to the task of decoding quantum polar codes and show that it inherits the high performance and low complexity of the classical case, and can approximate the quantum MLD for certain channels. We apply SCL decoding to a novel version of quantum polar codes based on the polarization weight (PW) method, which entirely avoids the need for small amounts of entanglement assistance apparent in previous quantum polar code constructions. When used to find the precise error pattern, the quantum SCL decoder (SCL-E) shows competitive performance with surface codes of similar size and low-density parity check codes of similar size and rate. The SCL decoder may instead be used to approximate the probability of each equivalence class of errors, and then choose the most likely class. We benchmark this class-oriented decoder (SCL-C) against the SCL-E decoder and find a noticeable improvement in the logical error rate. This improvement stems from the fact that the contributions from just the low-weight errors give a reasonable approximation to the error class probabilities. Both SCL-E and SCL-C maintain the complexity O(LN logN) of SCL for code size N and list size L. We also show that the list decoder can be used to gain insight into the weight distribution of the codes and how this impacts the effect of degenerate errors.

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

2 major / 2 minor

Summary. The manuscript adapts the classical successive cancellation list (SCL) decoder to quantum polar codes constructed via the polarization weight (PW) method, which the authors state avoids the entanglement assistance required in prior quantum polar constructions. Two variants are presented: SCL-E, which decodes to the most likely error pattern and achieves logical error rates competitive with surface codes and LDPC codes of comparable size and rate; and SCL-C, which approximates probabilities over error equivalence classes and yields a noticeable improvement in logical error rate. Both retain O(L N log N) complexity; the list decoder is additionally used to analyze weight distributions and the impact of degeneracy.

Significance. If the PW construction is valid, the work supplies an efficient, list-based decoder for quantum polar codes that directly exploits degeneracy via class-oriented decoding, producing measurable logical-error-rate gains over exact-pattern decoding while remaining competitive with established families. The explicit complexity bound and the use of the list to probe weight distributions are concrete strengths.

major comments (2)
  1. [§2] §2 (PW construction): the central performance claims for both SCL-E and SCL-C rest on the assertion that the polarization-weight method produces valid entanglement-free stabilizer codes with the stated distance and rate; the manuscript must supply an explicit verification that the resulting generators satisfy the required commutation relations, otherwise all decoder benchmarks against surface and LDPC codes are inapplicable.
  2. [§4.2] §4.2 and Table 1: the reported logical error rates for SCL-C versus SCL-E are presented without the underlying channel model parameters, code lengths, or list sizes used in the comparison; these omissions prevent independent assessment of whether the observed improvement is load-bearing or an artifact of the chosen simulation regime.
minor comments (2)
  1. [Figure 3] Figure 3 caption: the legend does not distinguish the three curves (SCL-E, SCL-C, and the surface-code reference) by line style or marker; this reduces readability.
  2. Notation: the symbol for the logical error rate is introduced inconsistently as P_L in the text and p_L in the figure axes; a single definition should be used throughout.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments. We address each major comment below and will revise the manuscript accordingly to strengthen its rigor and reproducibility.

read point-by-point responses
  1. Referee: [§2] §2 (PW construction): the central performance claims for both SCL-E and SCL-C rest on the assertion that the polarization-weight method produces valid entanglement-free stabilizer codes with the stated distance and rate; the manuscript must supply an explicit verification that the resulting generators satisfy the required commutation relations, otherwise all decoder benchmarks against surface and LDPC codes are inapplicable.

    Authors: We agree that an explicit verification of the commutation relations is necessary to confirm the validity of the PW construction. In the revised manuscript we will add a dedicated subsection (or appendix) to §2 that provides the algebraic verification that the stabilizer generators obtained via the polarization-weight method satisfy [G_i, G_j] = 0 for all i,j, thereby establishing that the resulting codes are valid entanglement-free stabilizer codes with the claimed distance and rate. This verification will be based on the recursive structure of the PW construction and will directly support the subsequent decoder benchmarks. revision: yes

  2. Referee: [§4.2] §4.2 and Table 1: the reported logical error rates for SCL-C versus SCL-E are presented without the underlying channel model parameters, code lengths, or list sizes used in the comparison; these omissions prevent independent assessment of whether the observed improvement is load-bearing or an artifact of the chosen simulation regime.

    Authors: We acknowledge the omission of the simulation parameters. In the revised version we will expand §4.2 and update Table 1 to explicitly report the channel model (depolarizing channel with error probability p), the code lengths N, the rates, and the list sizes L used for all SCL-C versus SCL-E comparisons. These additions will enable independent reproduction and evaluation of the reported logical-error-rate improvements. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation adapts external classical decoder to PW-based quantum codes with independent benchmarks

full rationale

The paper adapts the successive cancellation list decoder from the classical polar code literature to a novel PW-based quantum polar code construction that avoids entanglement assistance. Logical error rate results are obtained via simulation benchmarks against surface codes and LDPC codes of comparable size/rate; these are external comparisons, not reductions of fitted parameters or self-referential definitions. No equations, self-citations, or ansatzes are shown that would make any reported performance metric equivalent to its inputs by construction. The central claims rest on the validity of the PW construction and decoder adaptation, which are presented as independent of the reported error rates themselves.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated.

pith-pipeline@v0.9.0 · 5792 in / 1209 out tokens · 24834 ms · 2026-05-24T08:56:50.633443+00:00 · methodology

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Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages · 11 internal anchors

  1. [1]

    List decoding for noisy channels

    P . Elias, “List decoding for noisy channels”, Technical Report 335, Research Laboratory of Electronics, MIT (1957) (page 1)

  2. [2]

    List decoding

    J. M. Wozencroft, “List decoding”, Quarterly Progress Report, Research Laboratory of Electronics, MIT 48, 90–95 (1958) (page 1)

  3. [3]

    Guruswami,List Decoding of Error-correcting Codes, Lecture Notes in Computer Science, 3282 (Springer Berlin Heidelberg, Berlin, Heidelberg, May 1, 2005) (page 1)

    V . Guruswami,List Decoding of Error-correcting Codes, Lecture Notes in Computer Science, 3282 (Springer Berlin Heidelberg, Berlin, Heidelberg, May 1, 2005) (page 1)

  4. [4]

    Guruswami, A

    V . Guruswami, A. Rudra, and M. Sudan,Essential Coding Theory (2022) (page 1)

  5. [5]

    List Decoding of Polar Codes

    I. Tal and A. Vardy, “List Decoding of Polar Codes”, IEEE Transactions on Information Theory 61, 2213–2226 (2015), arXiv:1206.0050[cs.IT] (pages 1, 3). 12 0.06 0.08 0.1 0.12 0.14 0 0.2 0.4 0.6 Physical error rate p Logical error rate PL N 64 Method 128 SCL-E 256 SCL-C 512 Figure 7: Logical error rate of the SCL-E (solid) and the SCL-C (dotted) decoder for...

  6. [6]

    2016) (pages 1–3)

    Huawei and HiSilicon, Polar code design and rate matching, 3GPP TSG RAN WG1 Meeting #86 Report R1-167209 (Gothenburg, Sweden, Aug. 2016) (pages 1–3)

  7. [7]

    Polarization Weight Family Methods for Polar Code Construction

    Y. Zhou, R. Li, H. Zhang, H. Luo, and J. Wang, “Polarization weight family methods for polar code construction”, in 2018 IEEE 87th Vehicular Technology Conference (VTC spring) (June 2018), pp. 1– 5, arXiv:1805.02813 [cs.IT] (pages 1–3)

  8. [8]

    Efficient Quantum Polar Coding

    J. M. Renes, F . Dupuis, and R. Renner, “Efficient Polar Coding of Quantum Information”, Physical Review Letters 109, 050504 (2012), arXiv:1109.3195 [quant-ph] (page 1)

  9. [9]

    Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels

    E. Arıkan, “Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmet- ric Binary-Input Memoryless Channels”, IEEE Transactions on Information Theory 55, 3051–3073 (2009), arXiv:0807.3917 [cs.IT] (pages 1, 3)

  10. [10]

    Alignment of Polarized Sets

    J. M. Renes, D. Sutter, and S. H. Hassani, “Alignment of Polarized Sets”, IEEE Journal on Selected Areas in Communications 34, 224–238 (2016), arXiv:1411.7925[quant-ph] (page 1)

  11. [11]

    Good Quantum Error-Correcting Codes Exist

    A. R. Calderbank and P . W . Shor, “Good quantum error-correcting codes exist”, Physical Review A54, 1098 (1996), arXiv:quant-ph/9512032 (pages 1, 4)

  12. [12]

    Multiple Particle Interference and Quantum Error Correction

    A. Steane, “Multiple-particle interference and quantum error correction”, Proceedings of the Royal Society A 452, 2551–2577 (1996), arXiv:quant-ph/9601029 (pages 1, 4)

  13. [13]

    Gong, PW-QPC-List-Decoder: List Decoder for the Polarization Weight family of Quantum Polar Code

    A. Gong, PW-QPC-List-Decoder: List Decoder for the Polarization Weight family of Quantum Polar Code. GitHub, (2023) (pages 2, 3)

  14. [14]

    Application of Boolean algebra to switching circuit design and to error detection

    D. E. Muller, “Application of Boolean algebra to switching circuit design and to error detection”, Transactions of the I.R.E. Professional Group on Electronic Computers EC-3, 6–12 (1954) (page 3)

  15. [15]

    A class of multiple-error-correcting codes and the decoding scheme

    I. Reed, “A class of multiple-error-correcting codes and the decoding scheme”, Transactions of the IRE Professional Group on Information Theory 4, 38–49 (1954) (page 3)

  16. [16]

    $\beta$-expansion: A Theoretical Framework for Fast and Recursive Construction of Polar Codes

    G. He, J.-C. Belfiore, I. Land, G. Yang, X. Liu, Y. Chen, R. Li, J. Wang, Y. Ge, R. Zhang, and W . Tong, “Beta-Expansion: A Theoretical Framework for Fast and Recursive Construction of Polar Codes”, in 2017 IEEE Global Communications Conference (2017), pp. 1–6, arXiv:1704.05709[cs.IT] (page 3). 13

  17. [17]

    LLR-based Successive Cancellation List Decoding of Polar Codes

    A. Balatsoukas-Stimming, M. B. Parizi, and A. Burg, “LLR-Based Successive Cancellation List Decoding of Polar Codes”, IEEE Transactions on Signal Processing 63, 5165–5179 (2015), arXiv:1401.3753 [cs.IT] (page 3)

  18. [18]

    Fault-Tolerant Preparation of Quantum Polar Codes Encoding One Logical Qubit

    A. Goswami, M. Mhalla, and V . Savin, “Fault-Tolerant Preparation of Quantum Polar Codes Encoding One Logical Qubit”, arXiv:2209.06673 [quant-ph] (2022) (pages 5, 10–12)

  19. [19]

    List Decoding of Polar Codes: How Large Should the List Be to Achieve ML Decoding?

    A. Fazeli, A. Vardy, and H. Yao, “List Decoding of Polar Codes: How Large Should the List Be to Achieve ML Decoding?”, in 2021 IEEE International Symposium on Information theory (ISIT) (2021), pp. 1594–1599 (page 5)

  20. [20]

    Active stabilisation, quantum computation and quantum state synthesis

    A. M. Steane, “Active Stabilization, Quantum Computation, and Quantum State Synthesis”, Physical Review Letters 78, 2252–2255 (1997), arXiv:quant-ph/9611027 (page 6)

  21. [21]

    Efficient Algorithms for Maximum Likelihood Decoding in the Surface Code

    S. Bravyi, M. Suchara, and A. Vargo, “Efficient algorithms for maximum likelihood decoding in the surface code”, Physical Review A 90, 032326 (2014), arXiv:1405.4883 [quant-ph] (pages 7, 8)

  22. [22]

    Error-rate-agnostic decoding of topological stabilizer codes

    K. Hammar, A. Orekhov, P . W . Hybelius, A. K. Wisakanto, B. Srivastava, A. F . Kockum, and M. Granath, “Error-rate-agnostic decoding of topological stabilizer codes”, Physical Review A105, 042616 (2022), arXiv:2112.01977 [quant-ph] (page 8)

  23. [23]

    Decoding across the quantum low-density parity- check code landscape

    J. Roffe, D. R. White, S. Burton, and E. Campbell, “Decoding across the quantum low-density parity- check code landscape”, Physical Review Research 2, 043423 (2020), arXiv:2005.07016 [quant-ph] (pages 9, 10)

  24. [24]

    Quantum polar codes

    A. Goswami, “Quantum polar codes”, PhD thesis (University of Grenoble, Grenoble, 2021) (page 10)

  25. [25]

    Degenerate Quantum LDPC Codes With Good Finite Length Perfor- mance

    P . Panteleev and G. Kalachev, “Degenerate Quantum LDPC Codes With Good Finite Length Perfor- mance”, Quantum 5, 585 (2021), arXiv:1904.02703 [quant-ph] (page 10). 14