Model and algorithm of neural networks with quantum gated nodes

Title:

Model and algorithm of neural networks with quantum gated nodes

Creator:

Li, Panchi , Song, Kaoping, and Yang, Erlong

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:883a6180-51d2-4bcd-8395-1a5b58a42a1b
uuid:883a6180-51d2-4bcd-8395-1a5b58a42a1b

Subject:

Quantum computing, quantum gates, quantum neuron, quantum neural networks, and learning algorithm

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

On the basis of analyzing the principles of the quantum rotation gates and quantum controlled-NOT gates, an improved design for CNOT gated quantum neural networks model is proposed and a smart algorithm for it is derived in our paper, based on the gradient descent algorithm. In the improved model, the input information is expressed by the qubits, which, as the control qubits after being rotated by the rotation gate, control the qubits in the hidden layer to reverse. The qubits in the hidden layer, as the control qubits after being rotated by the rotation gate, control the qubits in the output layer to reverse. The networks output is described by the probability amplitude of state |1> in the output layer. It has been shown in two application examples of pattern recognition and function approximation that the proposed model is superior to the standard error back-propagation networks with regard to their convergence rate, number of iterations, approximation ability, and robustness.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:2

Harvested from:

CDK

Metadata only:

false