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Simulation of a Multidimensional Input Quantum Perceptron

  • Alexandre Y. Yamamoto
  • Kyle M. Sundqvist
  • Peng Li
  • H. Rusty HarrisEmail author
Article

Abstract

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).

Keywords

Perceptron Quantum machine learning Quantum Artificial Neural Network Quantum information Quantum Perceptron 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Alexandre Y. Yamamoto
    • 1
  • Kyle M. Sundqvist
    • 2
  • Peng Li
    • 1
  • H. Rusty Harris
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Department of PhysicsSan Diego State UniversitySan DiegoUSA

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