To solve the overlap of part of fault classes in the analog circuit fault diagnostics, a novel analog circuit fault diagnostics approach based on quantum neural networks algorithm was presented. Kurtosis and entropy were calculated as features after the time domain response signals of the circuit under test were measured, and then the different fault classes were identified by quantum neural networks algorithm. The simulation demonstrated that constructed neural network had simple network structure and the fault diagnosis accuracy was higher, which reached 99.62%.