Quanvolutional Neural Networks

TL;DR for operators Stacking quantum layers is easy. Training them is the awkward part. The paper behind ResQuNNs shows that ordinary multi-layer Quanvolutional Neural Networks, or QuNNs, can look deep while only the final quanvolutional layer actually receives useful gradients. The earlier layers are measured, converted into classical outputs, and then re-encoded before the next quantum circuit. That sequence breaks the differentiable path. The network may have several quantum layers, but optimisation treats most of them like decorative plumbing. ...