子系统多超立方体码的神经解码器
Neural decoders for subsystem many-hypercube codes
中文总结 AI 辅助
针对子系统多超立方体码的解码难题，开发电路级噪声模型下的神经网络解码器，利用规范测量信息提升性能，发现循环神经解码器优于全连接型且可解码更长序列。
中文摘要 AI 辅助
为充分发挥量子纠错码的潜力，开发高性能解码器至关重要。子系统多超立方体（MHC）码已被开发以实现高编码率和低重量的校验子测量，但规范自由度的引入使解码更具挑战性。本研究针对电路级噪声模型下的子系统MHC码开发基于神经网络的解码器，通过精心安排校验子测量序列，甚至可利用规范测量信息提升解码性能。我们进一步证明，循环神经解码器优于简单的全连接神经解码器，且能解码比训练时所用更长的校验子测量序列。
英文摘要
To maximize the potential of quantum error-correcting codes, it is essential to develop high-performance decoders. The subsystem many-hypercube (MHC) codes have been developed to achieve both high encoding rates and low-weight syndrome-measurements, but the introduction of gauge degrees of freedom makes decoding more challenging. In this work, we develop neural-network-based decoders for the subsystem MHC codes in a circuit-level noise model. We demonstrate that even the gauge-measurement information can be utilized for decoding by carefully arranging the syndrome-measurement sequence, improving the decoding performance. We further show that recurrent neural decoders outperform simple fully connected neural decoders, and can decode syndrome-measurement sequences longer than those used during training.
