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Preserving Data Locality in Multidimensional Variational Quantum Classification

SessionBest Research Poster Presentations

DescriptionIn classical machine learning, the convolution operation is leveraged in the eponymous class of convolutional neural networks (CNNs) capturing the spatial and/or temporal locality of multidimensional input features. Preserving data locality allows CNN models to reduce the number of training parameters, and hence their training time, while achieving high classification accuracy. However, contemporary methods of quantum machine learning do not possess effective methods for exploiting data locality, due to the lack of a generalized and parameterizable implementation of quantum convolution. In this work, we propose variational quantum classification techniques that leverage a novel multidimensional quantum convolution operation with arbitrary filtering and unity stride. We provide the quantum circuits for our techniques alongside corresponding theoretical analysis. We also experimentally demonstrate the advantage of our method in comparison with existing quantum and classical techniques for image classification in staple multidimensional datasets using state-of-the-art quantum simulations.

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