摘要
通过分析量子势阱粒子群优化算法的设计过程,提出一种基于Bloch球面搜索的量子粒子群优化算法.首先用基于Bloch球面描述的量子位描述粒子,用泡利矩阵建立旋转轴,用Delta势阱模型计算旋转角度,用量子位在Bloch球面上的绕轴旋转实现搜索.然后用Hadamard门实现粒子变异,以避免早熟收敛.这种旋转可使当前量子位沿着Bloch球面上的大圆逼近目标量子位,从而可加速优化进程.仿真结果表明,该算法的优化能力优于原算法.
To enhance optimization ability of quantum potential well-based particle swarm optimization algorithm,a quantum particle swarm optimization algorithm based on Bloch spherical search is proposed by analyzing the design of quantum potential well-based particle swarm optimization algorithms.Firstly,particles are expressed with qubits,axis of rotation is established with Pauli matrix,the angle of rotation is obtained with a model of Delta potential well,and search is realized with rotation of qubits in Bloch sphere.Then,to avoid premature convergence,mutation of particles is achieved with Hadamard gates.Such rotation makes current qubit approximates target qubit along with the biggest circle on Bloch sphere,which accelerates optimization process.It shows that the proposed algorithm is superior to the original one in optimization ability.
出处
《计算物理》
CSCD
北大核心
2013年第3期454-462,共9页
Chinese Journal of Computational Physics
基金
国家自然科学基金(61170132)资助项目
关键词
量子计算
量子势阱
Bloch球面搜索
粒子群优化
算法设计
quantum computation
quantum potential well
Bloch spherical search
particle swarm optimization
algorithm design
