Saq: Stabilizer-Aware Quantum Error Correction Decoder Achieves 18.9% Accuracy with Linear Scalability Via Transformer-based Learning
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Quantum error correction remains a critical challenge in building practical quantum computers, and researchers continually seek methods that balance decoding accuracy with computational efficiency. David Zenati and Eliya Nachmani, from Ben-Gurion University of the Negev, alongside their colleagues, present a new approach called SAQ-Decoder that significantly advances this field. This unified framework combines the power of transformer-based machine learning with carefully designed constraints, achieving decoding accuracy that rivals the most computationally intensive classical methods while maintaining linear scalability.
The team demonstrates near-optimal performance on standard quantum codes, reaching error thresholds that approach the theoretical limits and surpassing existing decoders in both accuracy and efficiency, establishing that learned decoders can meet the demanding requirements of fault-tolerant quantum computing.
Neural Network Decodes Quantum Surface Codes This research details a new neural network decoder, SAQ-Decoder, for correcting errors in quantum computations. The decoder achieves state-of-the-art performance, approaching the theoretical limits for decoding quantum surface codes, a promising architecture for building fault-tolerant quantum computers. Extensive experiments demonstrate its capabilities and provide detailed comparisons to existing decoding methods, both neural and classical. The study introduces SAQ-Decoder, a novel architecture designed to overcome the traditional trade-off between accuracy and efficiency in quantum error correction. The decoder processes information using a dual-stream transformer, simultaneously analyzing syndrome measurements and logical data to gain a comprehensive understanding of quantum errors. Asymmetric attention patterns within the network allow it to focus on the most relevant information, improving decoding performance. A novel loss function directly optimizes the correction of logical errors, a crucial step towards achieving high-fidelity quantum computation. Dual-Stream Transformer Enables Efficient Error Decoding Researchers have pioneered a new quantum error correction method, SAQ-Decoder, designed to overcome the accuracy-efficiency limitations of existing approaches. The decoder utilizes a dual-stream transformer architecture that simultaneously processes syndrome measurements and logical information, enabling a more holistic understanding of quantum errors. Asymmetric attention patterns allow the model to focus on the most relevant data, improving decoding performance. To achieve linear computational scalability with respect to syndrome size, scientists implemented a transformer-based learning framework, moving beyond the polynomial complexity of methods like Minimum Weight Perfect Matching and the high cost of tensor network decoders. The SAQ-Decoder incorporates specialized post-processing techniques to enforce constraints and refine decoding results, ensuring adherence to the underlying quantum error correction code. Experiments using toric codes, a prominent family of topological QEC codes, rigorously evaluate the decoder’s performance, achieving error thresholds of 10. 99% for independent noise and 18. 6% for depolarizing noise, approaching the theoretical Maximum Likelihood bounds. SAQ-Decoder Achieves Near-Optimal Quantum Error Correction The research team has developed SAQ-Decoder, a novel approach to quantum error correction that simultaneously achieves high accuracy and computational efficiency. This breakthrough addresses a fundamental challenge in building practical quantum computers, where maintaining the integrity of quantum information is paramount. The work demonstrates a unified framework combining transformer-based learning with a constraint-aware post-processing step, resulting in a decoder that scales linearly with the size of the quantum system. Experiments reveal that SAQ-Decoder achieves near-optimal performance on toric codes, reaching error thresholds of 10. 99% for independent noise and 18. 6% for depolarizing noise. These results are remarkably close to the theoretical maximum likelihood bounds, demonstrating the decoder’s ability to accurately identify and correct errors. The decoder’s performance matches that of computationally expensive methods while significantly outperforming existing neural and classical baselines in both accuracy and speed.
Near Maximum Likelihood Quantum Decoding Achieved SAQ-Decoder represents a significant advance in quantum error correction, achieving near-maximum likelihood accuracy alongside linear computational scaling with respect to syndrome size. This new framework combines transformer-based learning with a constraint-aware post-processing step, effectively bridging the gap between neural pattern recognition and the structured optimization demanded by quantum error correction. Experimental results demonstrate error thresholds of 10. 99% for independent noise and 18. 6% for depolarizing noise on toric codes, approaching the theoretical limits of maximum likelihood decoding while surpassing the performance of existing neural and classical decoders.
The team’s approach delivers both high accuracy and parameter efficiency, translating to reduced memory requirements, faster training, and improved suitability for deployment on quantum computing systems. This parameter efficiency stems from a novel logical class embedding strategy that avoids the quadratic scaling observed in some other methods. Future work may focus on extending this framework to more complex code families and exploring its performance in realistic quantum hardware environments. 👉 More information 🗞 SAQ: Stabilizer-Aware Quantum Error Correction Decoder 🧠 ArXiv: https://arxiv.org/abs/2512.08914 Tags: