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

Xin Li¹, Huangfu Xu², Xuezhong Guan²

¹School of Computer and Information Technology, Northeast Petroleum University, Daqing, China.
²School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China.

DOI: 10.4236/ojop.2015.42003                

Abstract

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.

Keywords

Quantum Computing, Particle Swarm Optimization, Multi-Qubits Probability Amplitudes Encoding, Algorithm Design

Conflicts of Interest

The authors declare no conflicts of interest.

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