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JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY

Hybrid Quantum-inspired Neural Networks Model and Algorithm

LI Pan-ch

Published Jan 1, 2016• 0 citations
OpenAlex

Abstract

To enhance the mapping ability of artificial neural networks, by studying the mapping mechanism of hidden layer neurons, a new idea of designing neural networks model based on rotation of qubits in the Bloch sphere is proposed in this paper. In the proposed approach, the samples are linearly transformed to quantum bit phase, and the qubits are rotated about three coordinate axes, respectively. The network parameters of hidden layer are the rotation angles. The spherical coordinates of qubits can be obtained by the projection measurement. The output of hidden layer neurons can be concluded by submitting these coordinates to excitation functions in hidden layer. The general neurons are applied to the output layer. The learning algorithms of the proposed model are designed based on the Levenberg-Marquardt(L-M) algorithm. The experimental results show that the proposed model is superior to the classical(L-M) algorthm in approximation ability, generalization ability, and robust performance.

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To enhance the mapping ability of artificial neural networks, by studying the mapping mechanism of hidden layer neurons, a new idea of designing neural networks model based on rotation of qubits in the Bloch sphere is proposed in this paper. In the proposed approach, the samples are linearly transformed to quantum bit phase, and the qubits are rotated about three coordinate axes, respectively. The network parameters of hidden layer are the rotation angles. The spherical coordinates of qubits can be obtained by the projection measurement. The output of hidden layer neurons can be concluded by submitting these coordinates to excitation functions in hidden layer. The general neurons are applied to the output layer. The learning algorithms of the proposed model are designed based on the Levenberg-Marquardt(L-M) algorithm. The experimental results show that the proposed model is superior to the classical(L-M) algorthm in approximation ability, generalization ability, and robust performance.
To enhance the approximation and generalization ability of classical artificial neural network (ANN) by employing the principles of quantum computation, a quantum-inspired neuron based on controlled-rotation gate is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-rotation gate after being rotated by the quantum rotation gates, control the target qubit for rotation. The model output is described by the probability amplitude of state |1〉 in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the quantum-inspired neurons to the hidden layer and the classical neurons to the output layer. An algorithm of QNNSI is derived by employing the Levenberg–Marquardt algorithm. Experimental results of some benchmark problems show that, under a certain condition, the QNNSI is obviously superior to the ANN.
To enhance the approximation and generalization ability of classical artificial neural networks,a quantum-inspired neural network model,whose input of each dimension is a discrete sequence,is proposed.This model concludes three layers,in which the hidden layer consists of quantum-inspired neurons,and the output layer consists of common neurons.The quantum-inspired neuron consists of the quantum rotation gates and the multi-qubits controlled-rotation gates.By using the information feedback of target qubit from output to input in multi-qubits controlled-rotation gate,the overall memory of input sequences is realized.The output of quantum-inspired neuron is obtained from the entanglements of multi-qubits in controlled-rotation gates.The learning algorithm is designed in detail according to the basic principles of quantum computation.The characteristics of input sequence can be effectively obtained by way ofbreadthanddepth.The simulation results show that,when the input nodes and the length of the sequence satisfy a certain relations,the proposed model is obviously superior to the common artificial neural networks.
To enhance the approximation and generalization ability of artificial neural network (ANN) by employing the principles of quantum rotation gate and controlled-not gate, a quantum-inspired neuron with sequence input is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-not gate after being rotated by the quantum rotation gates, control the target qubit for reverse. The model output is described by the probability amplitude of state in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the sequence input-based quantum-inspired neurons to the hidden layer and the classical neurons to the output layer, and a learning algorithm is derived by employing the Levenberg-Marquardt algorithm. Simulation results of benchmark problem show that, under a certain condition, the QNNSI is obviously superior to the ANN.
To enhance the approximation ability of neural networks, by introducing quantum rotation gates to the traditional BP networks, a novel quantum-inspired neural network model is proposed in this paper. In our model, the hidden layer consists of quantum neurons. Each quantum neuron carries a group of quantum rotation gates which are used to update the quantum weights. Both input and output layer are composed of the traditional neurons. By employing the back propagation algorithm, the training algorithms are designed. Simulation-based experiments using two application examples of pattern recognition and function approximation, respectively, illustrate the availability of the proposed model.
To enhance the approximation ability of traditional Artificial neural network (ANN), by introducing the quantum rotation gates and the multi-qubits controlled-NOT gates to ANN, we proposed a Sequence input-based quantum-inspired neural network (SIQNN). In our model, the hidden nodes are composed of some multi-qubits controlled-NOT gates, the inputs are described by the multi-dimensional discrete qubits sequences, the output nodes are the traditional neurons. The model parameters include the rotation angles of quantum rotation gates in hide layer and the weights in output layer. The learning algorithms were derived by employing the Levenberg-Marquardt algorithm. Simulation results of predicting the runoff of the Hongjiadu Reservoir show that, the SIQNN is obviously superior to the ANN.
A quantum neural networks model based on quantum neurons and traditional neurons is presented in this paper. The input of quantum neuron is real vector, its weight is quantum bits, its transforming function is an inner product operator and its output is a real number. The network includes three layers. Input layer is composed of traditional neurons that receive input information. Hidden layer is composed of quantum neurons that extract pattern feature of input information and transfer them to output layer. Output layer is composed of traditional neurons that export calculation result. The weightings of output layer are rectified by back propagation algorithm. The weightings of hidden layer are rectified by a group of quantum gates. A detailed learning algorithm is designed. Finally the availability of the model and algorithm is illustrated by two application examples of pattern recognition and functional approximation.