Building quantum neural networks based on swap test

Jian Zhao, Yuanhang Zhang, Changpeng Shao, Yuchun Wu, Guangcan Guo, Guoping Guo

Research output: Contribution to journalArticle (Academic Journal)peer-review

28 Citations (Scopus)
315 Downloads (Pure)


An artificial neural network, consisting of many neurons in different layers, is an important method to simulate the human brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is the inner product and the nonlinear operation is represented by an activation function. In this work, we introduce a kind of quantum neuron whose inputs and outputs are quantum states. The inner product and activation operator of the quantum neurons can be realized by quantum circuits. Based on the quantum neuron, we propose a model of a quantum neural network in which the weights between neurons are all quantum states. We also construct a quantum circuit to realize this quantum neural network model. A learning algorithm is proposed meanwhile. We show the validity of the learning algorithm theoretically and demonstrate the potential of the quantum neural network numerically.
Original languageEnglish
Pages (from-to)012334
Number of pages9
JournalPhysical Review A
Publication statusPublished - 23 Jul 2019


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