Efficient Soft-Output Guessing for Enhanced Quantum Tanner Code Decoding

• URL: http://arxiv.org/abs/2603.18318v1
• Date: Wed, 18 Mar 2026 22:00:57 GMT
• Title: Efficient Soft-Output Guessing for Enhanced Quantum Tanner Code Decoding
• Abstract summary: SOGRAND outperforms the standard belief propagation plus OSD baseline by up to three orders of magnitude in logical error rate.<n>We mitigate trapping sets and cycles, resulting in improved convergence.
• Score: 19.115625447007428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a generalized low-density parity-check decoding framework for quantum Tanner codes utilizing soft-output guessing random additive noise decoding (SOGRAND). By soft-output decoding entire component codes, we mitigate trapping sets and cycles, resulting in improved convergence. SOGRAND, combined with ordered statistic decoding (OSD) post-processing, outperforms the standard belief propagation plus OSD baseline by up to three orders of magnitude in logical error rate, providing a way forward for scalable decoding of the emerging class of Tanner-code-based quantum codes.

Related papers

• A Highly Accurate Fast Decoding Framework for QLDPC codes Accelerated by Noise Perturbation and Ensemble Decoding [0.0]
  Noise Assisted Ensemble Decoding (NAED) is a highly accurate decoding framework with a significant advantage in real-time speed.<n>NAED constructs an ensemble of Tanner forests, obtained as acyclic subgraphs of the original Tanner graph, and performs exact inference on each Tanner forest.
  arXiv  Detail & Related papers  (2026-08-10T13:18:21Z)
• Approximate level-by-level maximum-likelihood decoding based on the Chase algorithm for high-rate concatenated stabilizer codes [0.0]
  It is essential to encode logical qubits using quantum error-correcting codes.<n>High-rated codes have attracted attention due to theoretical advances in fault-tolerant protocols.<n>We propose a general, high-performance decoder for high-rated stabilizer codes.
  arXiv  Detail & Related papers  (2026-01-26T18:04:29Z)
• Power and Limitations of Linear Programming Decoder for Quantum LDPC Codes [0.30912596009895504]
  Decoding quantum error-correcting codes is a key challenge in enabling fault-tolerant quantum computation.<n>In this work, we uncover a key limitation of linear programming (LP) decoding for quantum low-density parity-check codes.<n>We incorporate a post-processing technique known as ordered statistics decoding (OSD) which significantly enhances LP decoding performance in practice.
  arXiv  Detail & Related papers  (2025-08-06T18:00:01Z)
• Fault Tolerant Decoding of QLDPC-GKP Codes with Circuit Level Soft Information [3.075816977152969]
  We study the performance of QLDPC-GKPd codes under circuit-level noise.<n>We show that real-time soft information is critical for decoding under circuit-level noise.
  arXiv  Detail & Related papers  (2025-05-09T19:11:36Z)
• Decoding Quantum LDPC Codes using Collaborative Check Node Removal [0.0]
  We present a strategy to improve the performance of the iterative decoders based on a collaborative way.<n>We show that an integration of information measurements (IM) for qubits and it's adjacent stabilizer checks, can be exploited to extract far better performing results.
  arXiv  Detail & Related papers  (2025-01-14T11:41:45Z)
• Decoding Quantum LDPC Codes Using Graph Neural Networks [52.19575718707659]
  We propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs) The proposed GNN-based QLDPC decoder exploits the sparse graph structure of QLDPC codes and can be implemented as a message-passing decoding algorithm.
  arXiv  Detail & Related papers  (2024-08-09T16:47:49Z)
• Learning Linear Block Error Correction Codes [62.25533750469467]
  We propose for the first time a unified encoder-decoder training of binary linear block codes. We also propose a novel Transformer model in which the self-attention masking is performed in a differentiable fashion for the efficient backpropagation of the code gradient.
  arXiv  Detail & Related papers  (2024-05-07T06:47:12Z)
• Efficient Encoder-Decoder Transformer Decoding for Decomposable Tasks [53.550782959908524]
  We introduce a new configuration for encoder-decoder models that improves efficiency on structured output and decomposable tasks. Our method, prompt-in-decoder (PiD), encodes the input once and decodes the output in parallel, boosting both training and inference efficiency.
  arXiv  Detail & Related papers  (2024-03-19T19:27:23Z)
• Single-shot decoding of good quantum LDPC codes [38.12919328528587]
  We prove that quantum Tanner codes facilitate single-shot quantum error correction (QEC) of adversarial noise. We show that in order to suppress errors over multiple repeated rounds of QEC, it suffices to run the parallel decoding algorithm for constant time in each round.
  arXiv  Detail & Related papers  (2023-06-21T18:00:01Z)
• Neural Belief Propagation Decoding of Quantum LDPC Codes Using Overcomplete Check Matrices [60.02503434201552]
  We propose to decode QLDPC codes based on a check matrix with redundant rows, generated from linear combinations of the rows in the original check matrix. This approach yields a significant improvement in decoding performance with the additional advantage of very low decoding latency.
  arXiv  Detail & Related papers  (2022-12-20T13:41:27Z)
• Denoising Diffusion Error Correction Codes [92.10654749898927]
  Recently, neural decoders have demonstrated their advantage over classical decoding techniques. Recent state-of-the-art neural decoders suffer from high complexity and lack the important iterative scheme characteristic of many legacy decoders. We propose to employ denoising diffusion models for the soft decoding of linear codes at arbitrary block lengths.
  arXiv  Detail & Related papers  (2022-09-16T11:00:50Z)
• Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization [93.95299500688286]
  We focus on prediction problems with structured outputs subject to output validity constraints. We propose composed fine-tuning, which fine-tunes a predictor composed with the pre-trained denoiser. For two-layer ReLU networks, we prove that composed fine-tuning significantly reduces the complexity of the predictor.
  arXiv  Detail & Related papers  (2020-06-29T17:14:35Z)
• On Sparsifying Encoder Outputs in Sequence-to-Sequence Models [90.58793284654692]
  We take Transformer as the testbed and introduce a layer of gates in-between the encoder and the decoder. The gates are regularized using the expected value of the sparsity-inducing L0penalty. We investigate the effects of this sparsification on two machine translation and two summarization tasks.
  arXiv  Detail & Related papers  (2020-04-24T16:57:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.