多比特概率幅编码的量子衍生粒子群优化算法

为了提高粒子群算法的优化能力, 提出一种新的量子衍生粒子群优化算法. 该方法采用多比特量子系统的基态概率幅对粒子编码, 基于自身最优粒子和全局最优粒子确定旋转角度, 采用基于张量积构造的多比特量子旋转门实施粒子的更新. 在每步迭代中, 只需更新粒子的一个量子比特相位, 即可更新该粒子上的所有概率幅. 标准函数极值优化的实验结果表明, 所提出算法的单步迭代时间较长, 但优化能力较同类算法有大幅度提高.

Quantum-inspired particle swarm optimization algorithm encoded by probability amplitudes of multi-qubits

To enhance the optimization ability of the particle swarm algorithm, a novel quantum-inspired particle swarm optimization algorithm is proposed. In this method, the particles are encoded by the probability amplitudes of the basic states of the multi-qubits system. The rotation angles of multi-qubits are determined based on the local optimum particle and the global optimal particle, and the multi-qubits rotation gates are employed to update the particles. At each of iteration, updating any a qubit can lead to update all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization shows that, although its single step iteration consumes a long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.