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Well Logging Technology

Quantum Neural Networks and Its Application in Complex Water Flooded Layer Recognition

Hongwei Wang

Published Jan 1, 2007• 0 citations
OpenAlex

Abstract

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.

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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.
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.
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 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 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.
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.