量子神经网络及其在复杂水淹层识别中的应用

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

提出一种量子BP网络模型及学习算法。基于量子力学中1位相移门和2位受控非门的通用性,构造出一种量子神经元模型和3层量子BP网络模型,量子神经元模型由输入、相移、聚合、翻转、输出等5部分组成。由量子神经元构造出3层量子BP网络模型,基于梯度下降法构造了该模型学习算法。将该模型及算法用于模拟油藏测井解释中测井曲线与水淹级别之间的映射关系,从而实现油藏测井解释中水淹层自动识别。实验结果表明,该方法对解决水淹层识别问题具有良好的适应性和实用性。

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 ap- plied 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.