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Simulation of a Multidimensional Input Quantum Perceptron

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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).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA

    Alexandre Y. Yamamoto, Peng Li & H. Rusty Harris

  2. Department of Physics, San Diego State University, San Diego, CA, 92182, USA

    Kyle M. Sundqvist

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  1. Alexandre Y. Yamamoto
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  2. Kyle M. Sundqvist
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  3. Peng Li
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  4. H. Rusty Harris
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Correspondence to H. Rusty Harris.

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Yamamoto, A.Y., Sundqvist, K.M., Li, P. et al. Simulation of a Multidimensional Input Quantum Perceptron. Quantum Inf Process 17, 128 (2018). https://doi.org/10.1007/s11128-018-1858-1

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  • DOI: https://doi.org/10.1007/s11128-018-1858-1

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