Quantum error correction is a key ingredient for large scale quantum computation, protecting logical information from physical noise by encoding it into many physical qubits. Topological stabilizer codes are particularly appealing due to their geometric locality and practical relevance. In these codes, stabilizer measurements yield a syndrome that must be decoded into a recovery operation, making decoding a central bottleneck for scalable real time operation. Existing decoders are commonly classified into two categories. Classical algorithmic decoders provide strong and well established baselines, but may incur substantial computational overhead at large code distances or under stringent latency constraints. Machine learning based decoders offer fast GPU inference and flexible function approximation, yet many approaches do not explicitly exploit the lattice geometry and local structure of topological codes, which can limit performance. In this work, we propose QuantumSMoE, a quantum vision transformer based decoder that incorporates code structure through plus shaped embeddings and adaptive masking to capture local interactions and lattice connectivity, and improves scalability via a mixture of experts layer with a novel auxiliary loss. Experiments on the toric code demonstrate that QuantumSMoE outperforms state-of-the-art machine learning decoders as well as widely used classical baselines.

翻译：量子纠错是大规模量子计算的关键要素，通过将逻辑信息编码到众多物理量子比特中，使其免受物理噪声的影响。拓扑稳定子码因其几何局域性和实际意义而尤为引人注目。在这类码中，稳定子测量产生一个综合征候，该症候必须被解码为恢复操作，这使得解码成为可扩展实时操作的核心瓶颈。现有解码器通常分为两类。经典算法解码器提供了强大且成熟的基准，但在大码距或严苛延迟约束下可能带来巨大的计算开销。基于机器学习的解码器支持快速的GPU推理和灵活的函数近似，但许多方法未能充分利用拓扑码的晶格几何和局部结构，这可能限制其性能。在本工作中，我们提出了QuantumSMoE，一种基于量子视觉Transformer的解码器，它通过十字形嵌入和自适应掩蔽来编码码结构，以捕获局部交互和晶格连通性，并通过包含新型辅助损失的混合专家层提高了可扩展性。在环面码上的实验表明，QuantumSMoE的性能优于最先进的机器学习解码器以及广泛使用的经典基准方法。