A quantum model of feed-forward neural networks with unitary learning algorithms

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

Abstract

Quantum neural networks (QNNs) are promised to be powerful computing devices that integrate the advantages of artificial neural networks (ANNs) and quantum computing. Due to the different dynamics between ANN and quantum computing, constructing a reasonable QNN model with efficient learning algorithms is still an open challenge. In this paper, we propose a new quantum model for feed-forward neural networks whose learning algorithm applies quantum superposition and parallelism features. This model contains classical feed-forward neural network and the amplitude encoding QNN model as special cases. Moreover, it inherits the advantages and avoids the disadvantages of the amplitude encoding model. We give a quantum-classical hybrid procedure to implement the learning algorithm. The result shows that we can train this QNN by a series of unitary operators efficiently.
Original languageEnglish
Article number102 (2020)
Number of pages17
JournalQuantum Information Processing
Volume19
DOIs
Publication statusPublished - 5 Feb 2020

Keywords

  • quantum algorithm
  • Quantum neural network
  • quantum computing

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