A Quantum 3D Convolutional Neural Network with Application in Video Classification

Quantum computing seeks to exploit the properties of quantum mechanics to perform computations at a fraction of the cost compared to the classical computing methods. Recently, quantum methods for machine learning have attracted the interest of researchers. Those methods aim to exploit, in the context of machine learning, the potential benefits that the quantum computers should be able to offer in the near future. A particularly interesting area of research in this direction, investigates the union of quantum machine learning models with Convolutional Neural Networks.
In this paper we develop a quantum counterpart of a 3D Convolutional Neural Network for video classification, dubbed Q3D-CNN. This is the first approach for quantum video classification we are aware of. Our model is based on previously proposed quantum machine learning models, where manipulation of the input data is performed in such a way that a fully quantum-mechanical neural network layer can be realized and used to form a Quantum Convolutional Neural Network. We augment this approach by introducing quantum-friendly operations during data-loading and appropriately manipulating the quantum network. We demonstrate the applicability of the proposed Q3D-CNN in video classification using videos from a publicly available dataset. We successfully classify the test dataset using two and three classes using the quantum network and its classical counterpart.