Simulation of a Multidimensional Input Quantum Perceptron

被引：0
作者：
Alexandre Y. Yamamoto
Kyle M. Sundqvist
Peng Li
H. Rusty Harris
机构：
[1] Texas A&M University,Department of Electrical and Computer Engineering
[2] San Diego State University,Department of Physics
来源：
Quantum Information Processing | 2018年 / 17卷
关键词：
Perceptron; Quantum machine learning; Quantum Artificial Neural Network; Quantum information; Quantum Perceptron;
D O I：
暂无
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摘要：
In this work, we demonstrate the improved data separation capabilities of the Multidimensional Input Quantum Perceptron (MDIQP), a fundamental cell for the construction of more complex Quantum Artificial Neural Networks (QANNs). This is done by using input controlled alterations of ancillary qubits in combination with phase estimation and learning algorithms. The MDIQP is capable of processing quantum information and classifying multidimensional data that may not be linearly separable, extending the capabilities of the classical perceptron. With this powerful component, we get much closer to the achievement of a feedforward multilayer QANN, which would be able to represent and classify arbitrary sets of data (both quantum and classical).
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