Abstract: [Objective] To improve the computational efficiency and accuracy of stress tensor inversion from fault-slip data, and to address the limitations of conventional grid search methods—namely high computational cost and susceptibility to local optima—an inversion approach based on intelligent optimization algorithms was investigated. [Methods and Process] A novel fault-slip data inversion method based on the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is proposed, in which the stress tensor is parameterized by four variables: three Euler angles (α, β, γ) representing the orientations of the principal stress axes and a shape factor (stress ratio Φ). A misfit function is constructed based on the angular deviation between the shear stress direction and the observed slip vector. To enhance convergence performance, an elite-guided learning strategy was adopted, incorporating a reward-penalty feedback mechanism and a tensor distance metric to quantify stress similarity. Multiple synthetic stress models were tested using a simulated fault-slip dataset, and the inversion performance of QPSO was compared with the conventional grid search method in terms of efficiency and accuracy. [Results] The results demonstrate that the proposed QPSO-based inversion method achieves a non-convergence rate below 8%, with the computational time reduced to approximately 1/27 of that required by the grid search approach. The method converges rapidly in high-dimensional, multimodal parameter spaces and accurately identifies normal, reverse, and strike-slip stress regimes. The clustering of inversion results is well defined, indicating strong stability and physical consistency. [Conclusion] The QPSO-based method exhibits significant advantages in stress tensor inversion from fault-slip data, including high computational efficiency, strong adaptability, and fast convergence. It provides effective technical support for regional paleo-stress field reconstruction and focal mechanism analysis, and offers methodological insights for the integration of intelligent optimization algorithms in geomechanics applications.