title: Single-hidden-layer feed-forward quantum neural network based on Grover learning.
abstract: In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning.
authors.affiliation: Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan.
authors.forename: Cheng-Yi
authors.initials: CY
authors.lastname: Liu
authors.forename: Chein
authors.initials: C
authors.lastname: Chen
authors.forename: Ching-Ter
authors.initials: CT
authors.lastname: Chang
authors.forename: Lun-Min
authors.initials: LM
authors.lastname: Shih
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated; D011789:Quantum Theory
keywords: Grover algorithm; Neural network; Quantum computing