基于量子位Bloch坐标的量子遗传算法及其应用

提出了一种基于量子位Bloch坐标的量子遗传算法. 该方法用量子位构成染色体; 用量子位的Bloch坐标构成染色体上的基因位; 用量子旋转门进行染色体上量子位的更新; 用量子非门进行染色体变异. 对于量子旋转门的转角大小及方向的确定, 提出了一种简易快捷的新方法; 对旋转和变异操作, 提出了基于量子位Bloch坐标的新算子. 该算法将量子位的3个Bloch 坐标都看作基因位, 每条染色体包含3条并列的基因链, 每条基因链代表1个优化解.在染色体数目相同时, 可加速优化进程. 以函数极值优化和神经网络权值优化为例, 仿真结果表明该方法在搜索能力和优化效率两个方面优于普通量子遗传算法和简单遗传算法.

Quantum genetic algorithm based on Bloch coordinates of qubits and its application

A novel quantum genetic algorithm is proposed based on the Bloch coordinates of qubits. The chromosome is comprised of qubits whose Bloch coordinates comprise gene chain. The quantum chromosomes are updated by quantum rotation gates, and are mutated by quantum non-gates. For the size and direction of rotation angle of quantum rotation gates, a simple determining method is proposed. For the rotation and mutation of qubits, two new operators are constructed based on Bloch coordinates of qubits. In this algorithm, the Bloch coordinates of each qubit are regarded as three paratactic genes, each chromosome contains three gene chains, and each of gene chains represents an optimization solution, which can accelerate the convergence process for the same number of chromosomes. By two application examples of function extrema and neural network weights optimization, the simulation results show that the approach is superior to the common quantum genetic algorithm and the simple genetic algorithm in both search capability and optimization efficiency.