  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.
