A blockBP decoder for the surface code
<|reference_start|>A blockBP decoder for the surface code: We present a new decoder for the surface code, which combines the accuracy of the tensor-network decoders with the efficiency and parallelism of the belief-propagation algorithm. Our main idea is to replace the expensive tensor-network contraction step in the tensor-network decoders with the blockBP algorithm - a recent approximate contraction algorithm, based on belief propagation. Our decoder is therefore a belief-propagation decoder that works in the degenerate maximal likelihood decoding framework. Unlike conventional tensor-network decoders, our algorithm can run efficiently in parallel, and may therefore be suitable for real-time decoding. We numerically test our decoder and show that for a large range of lattice sizes and noise levels it delivers a logical error probability that outperforms the Minimal-Weight-Perfect-Matching (MWPM) decoder, sometimes by more than an order of magnitude.<|reference_end|>
@article{kaufmann2024a,
    title={A blockBP decoder for the surface code},
    author={Aviad Kaufmann, Itai Arad},
    journal={arXiv preprint arXiv:2402.04834},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04834},
    primaryClass={quant-ph cs.IT math.IT}
}