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:目前的量子硬件受到各种噪音来源的影响,这些噪音限制了进入多位位相缠的状态。 使用单一的 qubit 瓶子的 Quantum 自动coder 电路已经显示有能力纠正杂乱纠缠状态中的错误。 通过在瓶颈(所谓的脑箱)中引入略为复杂的结构,拆卸过程可以更快地进行,并进入更强的噪音通道。 选择最适合的瓶子脑箱是硬件噪音强度与训练阻力之间的权衡的结果。 最后,通过研究整个网络的R\'enyi 酶流,我们证明纠结的本地化在通过学习解密方面起着核心作用。</s>