A quantum-implementable neural network model

Jialin Chen, Lingli Wang*, Edoardo Charbon

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    12 Citations (Scopus)

    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.

    Original languageEnglish
    Article number245
    Pages (from-to)1-24
    JournalQuantum Information Processing
    Volume16
    Issue number10
    DOIs
    Publication statusPublished - 2017

    Keywords

    • Feedforward neural network
    • Iris and MNIST experiments
    • Quantum neuron
    • Quantum probability neural network

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