Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data
This work addresses the need for more expressive quantum machine learning models for researchers in quantum computing, though it appears incremental as it builds on existing quantum convolutional networks.
The paper tackles the problem of improving quantum neural networks by studying multi-qubit interactions, introducing a Quantum Convolutional Network with novel Interaction layers that outperforms existing state-of-the-art methods on datasets like MNIST, Fashion MNIST, and Iris.
Quantum Machine Learning (QML) has come into the limelight due to the exceptional computational abilities of quantum computers. With the promises of near error-free quantum computers in the not-so-distant future, it is important that the effect of multi-qubit interactions on quantum neural networks is studied extensively. This paper introduces a Quantum Convolutional Network with novel Interaction layers exploiting three-qubit interactions, while studying the network's expressibility and entangling capability, for classifying both image and one-dimensional data. The proposed approach is tested on three publicly available datasets namely MNIST, Fashion MNIST, and Iris datasets, flexible in performing binary and multiclass classifications, and is found to supersede the performance of existing state-of-the-art methods.