Accelerating the Tesseract Decoder for Quantum Error Correction

• URL: http://arxiv.org/abs/2602.02985v2
• Date: Wed, 04 Feb 2026 21:52:54 GMT
• Title: Accelerating the Tesseract Decoder for Quantum Error Correction
• Authors: Dragana Grbic, Laleh Aghababaie Beni, Noah Shutty,
• Abstract summary: Tesseract is a novel Most-Likely-Error (MLE) decoder for Quantum Error Correction (QEC)<n>This paper presents a systematic approach to optimizing the Tesseract decoder through low-level performance enhancements.
• Score: 1.0037458982330154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Error Correction (QEC) is essential for building robust, fault-tolerant quantum computers; however, the decoding process often presents a significant computational bottleneck. Tesseract is a novel Most-Likely-Error (MLE) decoder for QEC that employs the A* search algorithm to explore an exponentially large graph of error hypotheses, achieving high decoding speed and accuracy. This paper presents a systematic approach to optimizing the Tesseract decoder through low-level performance enhancements. Based on extensive profiling, we implemented four targeted optimization strategies, including the replacement of inefficient data structures, reorganization of memory layouts to improve cache hit rates, and the use of hardware-accelerated bit-wise operations. We achieved significant decoding speedups across a wide range of code families and configurations, including Color Codes, Bivariate-Bicycle Codes, Surface Codes, and Transversal CNOT Protocols. Our results demonstrate consistent speedups of approximately 2x for most code families, often exceeding 2.5x. Notably, we achieved a peak performance gain of over 5x for the most computationally demanding configurations of Bivariate-Bicycle Codes. These improvements make the Tesseract decoder more efficient and scalable, serving as a practical case study that highlights the importance of high-performance software engineering in QEC and providing a strong foundation for future research.

Related papers

• A Mixture of Experts Vision Transformer for High-Fidelity Surface Code Decoding [12.998419492098462]
  Topological stabilizer codes are particularly appealing due to their geometric locality and practical relevance.<n>We propose QuantumSMoE, a quantum vision transformer based decoder that incorporates code structure through plus shaped embeddings.<n> Experiments on the toric code demonstrate that QuantumSMoE outperforms state-of-the-art machine learning decoders.
  arXiv  Detail & Related papers  (2026-01-18T16:49:59Z)
• Fast correlated decoding of transversal logical algorithms [67.01652927671279]
  Quantum error correction (QEC) is required for large-scale computation, but incurs a significant resource overhead.<n>Recent advances have shown that by jointly decoding logical qubits in algorithms composed of logical gates, the number of syndrome extraction rounds can be reduced.<n>Here, we reform the problem of decoding circuits by directly decoding relevant logical operator products as they propagate through the circuit.
  arXiv  Detail & Related papers  (2025-05-19T18:00:00Z)
• Bubble Clustering Decoder for Quantum Topological Codes [8.62986288837424]
  We introduce the bubble clustering decoder for quantum surface codes, which serves as a low-latency replacement for MWPM.<n>This speed boost is obtained leveraging an efficient cluster generation based on bubbles centered on defects.<n>For moderate physical error rates, this is equivalent to linear complexity in the number of data qubits.
  arXiv  Detail & Related papers  (2025-04-02T12:02:34Z)
• Local Clustering Decoder: a fast and adaptive hardware decoder for the surface code [0.0]
  We introduce the Local Clustering Decoder as a solution that simultaneously achieves the accuracy and speed requirements of a real-time decoding system. Our decoder is implemented on FPGAs and exploits hardware parallelism to keep pace with the fastest qubit types. It enables one million error-free quantum operations with 4x fewer physical qubits when compared to standard non-adaptive decoding.
  arXiv  Detail & Related papers  (2024-11-15T16:43:59Z)
• Accelerating Error Correction Code Transformers [56.75773430667148]
  We introduce a novel acceleration method for transformer-based decoders. We achieve a 90% compression ratio and reduce arithmetic operation energy consumption by at least 224 times on modern hardware.
  arXiv  Detail & Related papers  (2024-10-08T11:07:55Z)
• 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)
• Extreme Encoder Output Frame Rate Reduction: Improving Computational Latencies of Large End-to-End Models [59.57732929473519]
  We apply multiple frame reduction layers in the encoder to compress encoder outputs into a small number of output frames. We demonstrate that we can generate one encoder output frame for every 2.56 sec of input speech, without significantly affecting word error rate on a large-scale voice search task.
  arXiv  Detail & Related papers  (2024-02-27T03:40:44Z)
• Practical Conformer: Optimizing size, speed and flops of Conformer for on-Device and cloud ASR [67.63332492134332]
  We design an optimized conformer that is small enough to meet on-device restrictions and has fast inference on TPUs. Our proposed encoder can double as a strong standalone encoder in on device, and as the first part of a high-performance ASR pipeline.
  arXiv  Detail & Related papers  (2023-03-31T23:30:48Z)
• Improved decoding of circuit noise and fragile boundaries of tailored surface codes [61.411482146110984]
  We introduce decoders that are both fast and accurate, and can be used with a wide class of quantum error correction codes. Our decoders, named belief-matching and belief-find, exploit all noise information and thereby unlock higher accuracy demonstrations of QEC. We find that the decoders led to a much higher threshold and lower qubit overhead in the tailored surface code with respect to the standard, square surface code.
  arXiv  Detail & Related papers  (2022-03-09T18:48:54Z)

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.