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Modern Computer

A Quantum Neural Network Model and Its Improved Learning Algorithm

Hua Wan

Published Jan 1, 2010• 0 citations
OpenAlex

Abstract

Presents a quantum BP neural network model and its improved learning algorithm.Firstly a quantum neuron is constructed based on the universality of single qubit rotation gate and two-qubit controlled not gate.Secondly,the hide layer is constructed and uses these quantum neurons with the application of the gradient descent algorithm and the output layer is made with traditional neurons,in which weight and bias values are updated according to the improved algorithm of gradient descent momentum and an adaptive learning rate.It is shown that this model and algorithm are superior to the conventional BP networks in two aspects:convergence speed and accuracy by two UCI's data sets examples.

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Presents a quantum BP neural network model and its improved learning algorithm.Firstly a quantum neuron is constructed based on the universality of single qubit rotation gate and two-qubit controlled not gate.Secondly,the hide layer is constructed and uses these quantum neurons with the application of the gradient descent algorithm and the output layer is made with traditional neurons,in which weight and bias values are updated according to the improved algorithm of gradient descent momentum and an adaptive learning rate.It is shown that this model and algorithm are superior to the conventional BP networks in two aspects:convergence speed and accuracy by two UCI's data sets examples.
A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which |1〉 is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects: convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.
A quantum neural networks model and its learning algorithm are presented.Firstly,a quantum controlled-rotating gate is proposed by analyzing the meaning of the controlled-NOT gate.Then a quantum neuron model is constructed from the physical meaning of the controlled rotating gate.The model includes two kinds of design parameters,rotation angle and its control range.Secondly,a quantum neural networks model based on quantum neuron is proposed.By using gradient descent algorithm,a learning algorithm of this model is designed in detail.With applications of pattern recognition and time series prediction,the simulation results show that the proposed algorithm is superior to the common BP neural networks in both convergence capability and robustness.
A novel neural networks model with quantum weights is present.Firstly, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and inspiriting.Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real vectors; the linked weight and the activation value are qubits.On the basic of the gradient descent algorithm, a learning algorithm of this model is proposed.It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting the global optimal solution.Finally, the availability of the model is illustrated in both convergence speed and convergence rate by two application examples of pattern recognition and function approximation.
A quantum BP neural networks model with learning algorithm is proposed.Firstly,based on the universality of single qubit rotation gate and two-qubits controlled NOT gate,a quantum neuron model is constructed,which is composed of input,phase rotation,aggregation,reversal rotation,and output.In this model,both the input and the output are described by qubits.The phase rotation and inversion are performed by the universal quantum gates.Secondly,the quantum BP neural networks model based on quantum neurons is constructed.On the basic of the gradient descent algorithm,a learning algorithm of the model is proposed.The model is applied to simulate the relation between well logging and water-flooded level which can achieve so that water-flooded layer recognition can be achieved with in oil well logging.Testing results indicate that the method is effective.
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.