基于Bloch球面坐标的量子粒子群算法

为了提高粒子群优化(PSO)算法的优化效率，结合量子理论提出一种基于Bloch球面坐标的量子粒子群优化算法。在Bloch球面坐标下，粒子自动更新旋转角大小和粒子位置，不需将旋转角以查询表的形式设定(或设定为区间上的固定值)，弥补了Bloch球面坐标下量子进化算法和量子遗传算法的不足，算法更具有普遍性;用量子Hadamard门实现粒子的变异，增强了种群的多样性，促使粒子跳出局部极值点。对典型函数优化问题的仿真结果表明，提出的算法稳定性强，精度高，收敛速度快，具有一定的实用价值。

Quantum particle swarm optimization based on Bloch coordinates of qubits

To improve the efficiency of Particle Swarm Optimization (PSO), a quantum particle swarm optimization algorithm combined with quantum theory on the basis of Bloch sphere was proposed. In Bloch spherical coordinates, the particle automatically updated rotation angle and particle position, without setting the rotation angle in the form of look-up table (or setting fixed value of the interval), making up for the deficiency of quantum evolutionary algorithm and quantum genetic algorithm on the basis of Bloch sphere, and the algorithm is more generalizable. Using quantum Hadamard gate to realize the variation of particle enhanced the diversity of population, and prompted particle jump out of local extreme value. The simulation results of the typical function optimization problem show that the algorithm is stable with high precision and fast convergence rate, and it is practical.