Efficient and Universal Neural-Network Decoder for Stabilizer-Based Quantum Error Correction

2025-02-27

AI Summary

Quantum error correction is hindered by the lack of efficient, general-purpose decoders for emerging quantum low-density parity-check (QLDPC) codes. This work introduces the first graph-structure-aware neural decoder applicable to arbitrary stabilizer codes, departing from conventional code-specific design paradigms. The method integrates stabilizer code graph encoding, graph neural networks, and linear attention mechanisms to directly model the topological and relational structure of the syndrome graph. It achieves end-to-end decoding with linear time complexity and cross-code compatibility—requiring no architectural modification across code families. Evaluated on a bivariate bicycle code (distance d = 12), it reduces logical error rate by 39.4% and cuts decoding latency to just 1% of the current state-of-the-art. Crucially, the same decoder generalizes seamlessly to surface codes, color codes, and diverse QLDPC code families, markedly enhancing scalability and practical deployability in fault-tolerant quantum computing.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Quantum error correction is crucial for large-scale quantum computing, but the absence of efficient decoders for new codes like quantum low-density parity-check (QLDPC) codes has hindered progress. Here we introduce a universal decoder based on linear attention sequence modeling and graph neural network that operates directly on any stabilizer code's graph structure. Our numerical experiments demonstrate that this decoder outperforms specialized algorithms in both accuracy and speed across diverse stabilizer codes, including surface codes, color codes, and QLDPC codes. The decoder maintains linear time scaling with syndrome measurements and requires no structural modifications between different codes. For the Bivariate Bicycle code with distance 12, our approach achieves a 39.4% lower logical error rate than previous best decoders while requiring only ~1% of the decoding time. These results provide a practical, universal solution for quantum error correction, eliminating the need for code-specific decoders.

Problem

• Universal decoder for stabilizer codes
• Improves accuracy and speed
• Eliminates need for code-specific decoders

Innovation

• Universal decoder for quantum codes
• Linear attention sequence modeling
• Graph neural network application