Quantum-Inspired Particle Swarm Optimization Algorithm Encoded by Probability Amplitudes of Multi-Qubits

• 作者: Huangfu Xu Xin Li Xuezhong Guan
• 来源期刊: 最优化(英文)
• 学科: 医学
• 关键词: QUANTUM Computing Particle SWARM Optimization Multi-Qubits PROBABILITY AMPLITUDES Encoding Algorithm Design
• 年，卷（期）: 2015,（2）
• 页码范围: 21-30
• 页数: 10页
• 分类号: R73

摘要

To enhance the optimization ability of 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 qubit can lead to updating all probability amplitudes of the corresponding particle. The experimental results of some benchmark functions optimization show that, although its single step iteration consumes long time, the optimization ability of the proposed method is significantly higher than other similar algorithms.