Efficient quantum error correction is essential for the advancement of quantum computing. We propose a quantum neural network with a global structure that reduces the number of unitary matrices required in quantum circuits. This approach resulted in a 97% reduction in training time and up to a 25% improvement in the training completion rate, ultimately achieving a 100% success rate in training while surpassing the error correction performance reported in previous studies. In addition, we demonstrated the enhanced robustness of quantum error correction against internal network noise. Moreover, the fidelity of quantum error correction under internal network noise increased by up to 15% due to the reduced computational load.

翻译：高效的量子纠错对于量子计算的发展至关重要。我们提出了一种具有全局结构的量子神经网络，该网络减少了量子电路中所需的酉矩阵数量。这种方法使训练时间减少了97%，训练完成率最高提升了25%，最终实现了100%的训练成功率，同时超越了先前研究中报告的纠错性能。此外，我们证明了量子纠错在内部网络噪声下具有更强的鲁棒性。而且，由于计算负载的降低，内部网络噪声下的量子纠错保真度最高提升了15%。