Double Chains Quantum Genetic Algorithm with Application in Training of Process Neural Networks

Abstract

To address training of process neural networks based on the orthogonal basis expansion, a double chains quantum genetic algorithm based on the probability amplitudes of quantum bits is proposed. In this method, the probability amplitudes of each qubit are regarded as two paratactic genes, each chromosome contains two gene chains, and each of gene chains represents an optimization solution. The number of genes is determined by the number of weight parameters. Taking each qubit in the optimal chromosome as the goal, individuals are updated by quantum rotation gates, and mutated by quantum non-gates to increase the diversity of population. Taking the pattern classification of two groups of two-dimensional trigonometric functions as an example, the simulation results show that the proposed method is effective and efficient.