Quantum error correction is a critical component for scaling up quantum
computing. Given a quantum code, an optimal decoder maps the measured code
violations to the most likely error that occurred, but its cost scales
exponentially with the system size. Neural network decoders are an appealing
solution since they can learn from data an efficient approximation to such a
mapping and can automatically adapt to the noise distribution. In this work, we
introduce a data efficient neural decoder that exploits the symmetries of the
problem. We characterize the symmetries of the optimal decoder for the toric
code and propose a novel equivariant architecture that achieves state of the
art accuracy compared to previous neural decoders.

本文介绍了一种利用问题对称性的、数据效率高的神经网络译码器来纠正量子计算中的错误，该译码器可以自适应噪声分布，能够与先前的神经网络译码器相比实现最先进的准确性。

END: 量子纠错等变神经解码器

The END: An Equivariant Neural Decoder for Quantum Error Correction

We study the ability of linear recurrent networks obeying discrete time
dynamics to store long temporal sequences that are retrievable from the
instantaneous state of the network. We calculate this temporal memory capacity
for both distributed shift register and random orthogonal connectivity
matrices. We show that the memory capacity of these networks scales with system
size.

研究线性循环网络在遵守离散时间动力学的情况下，存储可从网络瞬时状态中检索的长时间序列的能力。 计算分布式移位寄存器和随机正交连接矩阵的时间记忆容量。 随着系统规模的增大，这些网络的记忆容量呈比例关系。