Abstract: To enhance the performance of the intelligent optimization algorithm, we propose a new model for performing asearch on a Bloch sphere. Then, by integrating this model into the artificial fish swarm optimization, we present a quantum-inspired artificial fish swarm optimization algorithm. In the proposed method, the fishes are encoded with qubits described on the Bloch sphere. Vector product theory is adopted to establish the rotation axis, and the Pauli matrices are used to construct the rotation matrices. The four fish behaviors, moving, tracking, capturing and aggregating, are achieved by rotating the current qubit about the rotation axis towards the target qubit on the Bloch sphere. The Bloch coordinates of the qubit can be obtained by measurment with the Pauli matrices, and the optimization solutions can be presented through the solution space transformation. The highlight advantages of this method are the ability to simultaneously adjust two parameters of a qubit and automatically achieve the best match between two adjustment quantities, which may accelerate the optimization process. The experiment results show that the proposed method obviously outperforms the classical one in convergence speed, and achieves better levels for some benchmark functions.