A Symmetry-Integrated Approach to Surface Code Decoding

• URL: http://arxiv.org/abs/2509.10164v1
• Date: Fri, 12 Sep 2025 11:41:49 GMT
• Title: A Symmetry-Integrated Approach to Surface Code Decoding
• Authors: Hoshitaro Ohnishi, Hideo Mukai,
• Abstract summary: Surface code is considered to be a promising encoding method with a high error threshold.<n>Previous methods have suffered from the problem that the decoder acquires solely the error probability distribution.<n>We propose a technique to reoptimize the decoder model by approximating syndrome measurements with a continuous function.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum error correction, which utilizes logical qubits that are encoded as redundant multiple physical qubits to find and correct errors in physical qubits, is indispensable for practical quantum computing. Surface code is considered to be a promising encoding method with a high error threshold that is defined by stabilizer generators. However, previous methods have suffered from the problem that the decoder acquires solely the error probability distribution because of the non-uniqueness of correct prediction obtained from the input. To circumvent this problem, we propose a technique to reoptimize the decoder model by approximating syndrome measurements with a continuous function that is mathematically interpolated by neural network. We evaluated the improvement in accuracy of a multilayer perceptron based decoder for code distances of 5 and 7 as well as for decoders based on convolutional and recurrent neural networks and transformers for a code distance of 5. In all cases, the reoptimized decoder gave better accuracy than the original models, demonstrating the universal effectiveness of the proposed method that is independent of code distance or network architecture. These results suggest that re-framing the problem of surface code decoding into a regression problem that can be tackled by deep learning is a useful strategy.

Related papers

• USDs: A universal stabilizer decoder framework using symmetry [0.0]
  We show that a technique previously shown to be effective for the Toric code can be generalized to address the challenge of label degeneracy.<n>For the Color code, we achieved an improvement of approximately 0.8% in decoding accuracy at a physical error rate of 5%, while for the Golay code the accuracy increased by about 0.1%.
  arXiv  Detail & Related papers  (2026-01-21T12:06:17Z)
• Learning Neural Decoding with Parallelism and Self-Coordination for Quantum Error Correction [7.184133388805955]
  Existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time.<n>We address this issue by training a recurrent, transformer-based neural network specifically tailored for sliding-window decoding.<n>This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments.
  arXiv  Detail & Related papers  (2025-09-04T02:02:27Z)
• Testing the Accuracy of Surface Code Decoders [55.616364225463066]
  Large-scale, fault-tolerant quantum computations will be enabled by quantum error-correcting codes (QECC) This work presents the first systematic technique to test the accuracy and effectiveness of different QECC decoding schemes.
  arXiv  Detail & Related papers  (2023-11-21T10:22:08Z)
• A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction [0.0]
  We design and analyze a neural decoder based on an in-memory crossbar (IMC) architecture. We develop hardware-aware re-training methods to mitigate the fidelity loss. This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated fault-tolerant QEC.
  arXiv  Detail & Related papers  (2023-07-18T17:46:33Z)
• Neural network decoder for near-term surface-code experiments [0.7100520098029438]
  Neural-network decoders can achieve a lower logical error rate compared to conventional decoders. These decoders require no prior information about the physical error rates, making them highly adaptable.
  arXiv  Detail & Related papers  (2023-07-06T20:31:25Z)
• Data-driven decoding of quantum error correcting codes using graph neural networks [0.0]
  We explore a model-free, data-driven, approach to decoding, using a graph neural network (GNN)<n>We show that the GNN-based decoder can outperform a matching decoder for circuit level noise on the surface code given only simulated data.<n>The results show that a purely data-driven approach to decoding may be a viable future option for practical quantum error correction.
  arXiv  Detail & Related papers  (2023-07-03T17:25:45Z)
• The END: An Equivariant Neural Decoder for Quantum Error Correction [73.4384623973809]
  We introduce a data efficient neural decoder that exploits the symmetries of the problem. We propose a novel equivariant architecture that achieves state of the art accuracy compared to previous neural decoders.
  arXiv  Detail & Related papers  (2023-04-14T19:46:39Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• 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)
• Graph Neural Networks for Channel Decoding [71.15576353630667]
  We showcase competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes. The idea is to let a neural network (NN) learn a generalized message passing algorithm over a given graph. We benchmark our proposed decoder against state-of-the-art in conventional channel decoding as well as against recent deep learning-based results.
  arXiv  Detail & Related papers  (2022-07-29T15:29:18Z)
• 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.