Quantum Convolutional Neural Network Architecture for Multi-Class Classification

Kashyap, S and Garani, SS (2023) Quantum Convolutional Neural Network Architecture for Multi-Class Classification. In: 2023 International Joint Conference on Neural Networks, IJCNN 2023, 18 - 23 June 2023, Gold Coast.

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Abstract

We propose quantum circuit architectures for convolutional neural networks based on generalized 3-qubit and 2-qubit quantum gates for the multiclass classification problem. The quantum architecture is equivalent to a classical convolutional neural network with fully connected layers and densely connected layers. The quantum circuit parameters are optimized by minimizing the cross-entropy loss function. We validate the classification performance over several model configurations on the MNIST, Fashion-MNIST and Kuzushiji-MNIST datasets. Our proposed architecture shows classification accuracies that are comparable to classical CNNs with a similar number of parameters. In addition to this, we find that circuit depth is greatly decreased by a logarithmic factor compared to classical CNNs. We study the performance and complexity tradeoffs over several model configurations within the proposed quantum CNN architecture. © 2023 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings of the International Joint Conference on Neural Networks
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the Institute of Electrical and Electronics Engineers Inc.
Keywords:	Classification (of information); Convolution; Multilayer neural networks; Network architecture, Circuit architectures; Convolutional neural network; Model configuration; Multi-class classification; Multiclass classification problems; Network-based; Neural network architecture; Quantum architecture; Quantum circuit; Quantum gates, Convolutional neural networks
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	28 Oct 2023 06:47
Last Modified:	28 Oct 2023 06:47
URI:	https://eprints.iisc.ac.in/id/eprint/83169

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