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.