期刊
PHYSICS LETTERS A
卷 379, 期 7, 页码 660-663出版社
ELSEVIER
DOI: 10.1016/j.physleta.2014.11.061
关键词
Quantum neural network; Quantum machine learning; Quantum computing; Linear classification
资金
- South African Research Chair Initiative of the Department of Science and Technology, Republic of South Africa
- National Research Foundation
Perceptrons are the basic computational unit of artificial neural networks, as they model the activation mechanism of an output neuron due to incoming signals from its neighbours. As linear classifiers, they play an important role in the foundations of machine learning. In the context of the emerging field of quantum machine learning, several attempts have been made to develop a corresponding unit using quantum information theory. Based on the quantum phase estimation algorithm, this paper introduces a quantum perceptron model imitating the step-activation function of a classical perceptron. This scheme requires resources in O(n) (where n is the size of the input) and promises efficient applications for more complex structures such as trainable quantum neural networks. (C) 2014 Elsevier B.V. All rights reserved.
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