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Quantum Information Processing
Published in: Quantum Information Processing 10/2017

01-10-2017

A quantum-implementable neural network model

Authors: Jialin Chen, Lingli Wang, Edoardo Charbon

Published in: Quantum Information Processing | Issue 10/2017

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Abstract

A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.
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Metadata
Title
A quantum-implementable neural network model
Authors
Jialin Chen
Lingli Wang
Edoardo Charbon
Publication date
01-10-2017
Publisher
Springer US
Published in
Quantum Information Processing / Issue 10/2017
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-017-1692-x

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