Current quantum hardware is subject to various sources of noise that limits the access to multi-qubit entangled states. Quantum autoencoder circuits with a single qubit bottleneck have shown capability to correct error in noisy entangled state. By introducing slightly more complex structures in the bottleneck, the so-called brainboxes, the denoising process can take place faster and for stronger noise channels. Choosing the most suitable brainbox for the bottleneck is the result of a trade-off between noise intensity on the hardware, and the training impedance. Finally, by studying R\'enyi entropy flow throughout the networks we demonstrate that the localization of entanglement plays a central role in denoising through learning.

翻译：当前量子硬件受限于多种噪声源，限制了对多量子比特纠缠态的访问。具有单量子比特瓶颈的量子自编码器电路已展现出纠正含噪纠缠态误差的能力。通过在瓶颈中引入更复杂的结构——即所谓的"脑盒"，去噪过程能够以更快的速度在更强噪声通道中实现。选择最适配瓶颈的脑盒需权衡硬件噪声强度与训练阻抗。最后，通过研究网络中的Rényi熵流，我们证明纠缠局域化在学习驱动的去噪过程中起核心作用。