Letao Wang

We introduce a Convolutional and a measurement based Pooling layer that offer polynomial advantages over their classical analogs. By conserving the subspace preserving structure of the state during the computation, these layers can be assembled to perform complex deep-learning algorithms such as Convolutional Neural Network architectures, while assuring the correct training of the quantum circuit. In particular, those circuits can avoid Barren Plateau by only considering subspaces of polynomial size, limiting the potential running time advantages to polynomial ones. Recent work has pointed out the link between the absence of Barren Plateau and a non-exponential advantage in the near-term QML literature, and we believe that our proposal offers a promising path for useful QML algorithms by optimizing the framework that avoids vanishing gradient phenomena. Our works also deal with an important question that only a few works address due to hardware limitations: how to ensure that a method's performance will scale with the size of the problems? By offering software tools that are tailored for Hamming-Weight preserving algorithms, and by mimicking the behavior of state-of-the-art classical deep-learning layers, we offer a solution that performs well in comparison with classical methods while offering an interesting running time advantage. Our software, that can be accessed through, allowed us to train our model on 10-label classification tasks that are far more complex than usual binary classification tasks used to illustrate QML methods and are commonly used in the classical Machine Learning literature.