Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning

• URL: http://arxiv.org/abs/2203.15368v1
• Date: Tue, 29 Mar 2022 09:07:18 GMT
• Title: Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning
• Authors: Denis Bokhan, Alena S. Mastiukova, Aleksey S. Boev, Dmitrii N. Trubnikov, Aleksey K. Fedorov
• Abstract summary: We propose a quantum machine learning approach based on quantum convolutional neural networks for solving multiclass classification problems. We use the proposed approach to demonstrate the 4-class classification for the case of the MNIST dataset using eight qubits for data encoding and four acnilla qubits. Our results demonstrate comparable accuracy of our solution with classical convolutional neural networks with comparable numbers of trainable parameters.
• Score: 0.5999777817331318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiclass classification is of great interest for various machine learning applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving this problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to softmax cost function with subsequent minimization of it via optimization of parameters of quantum circuit. Our conceptional improvements include a new model for quantum perceptron and optimized structure of the quantum circuit. We use the proposed approach to demonstrate the 4-class classification for the case of the MNIST dataset using eight qubits for data encoding and four acnilla qubits. Our results demonstrate comparable accuracy of our solution with classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our finding provide a new step towards the use of quantum machine learning for solving practically relevant problems in the NISQ era and beyond.

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