Advantages of versatile neural-network decoding for topological codes
This work addresses the problem of designing efficient decoders for quantum error correction in topological codes, which is incremental as it applies existing neural methods to new noise scenarios.
The authors tackled the challenge of efficient error correction in topological stabilizer codes by systematically studying neural-network-based decoders, reporting significant improvements in error-correction thresholds over leading efficient decoders under various realistic noise models.
Finding optimal correction of errors in generic stabilizer codes is a computationally hard problem, even for simple noise models. While this task can be simplified for codes with some structure, such as topological stabilizer codes, developing good and efficient decoders still remains a challenge. In our work, we systematically study a very versatile class of decoders based on feedforward neural networks. To demonstrate adaptability, we apply neural decoders to the triangular color and toric codes under various noise models with realistic features, such as spatially-correlated errors. We report that neural decoders provide significant improvement over leading efficient decoders in terms of the error-correction threshold. Using neural networks simplifies the process of designing well-performing decoders, and does not require prior knowledge of the underlying noise model.