We present an analog circuit fault diagnosis approach by using the double chain quantum genetic algorithm to optimize the simple multi-kernel learning support vector machine (DCQGA-SMKL-SVM). Firstly, we extract the time domain response signals of the test circuit, and utilize the Harr wavelet to process and normalize response signals to obtain characteristic parameters. Then, the parameters of the SMKL-SVM are optimized by the DCQGA and an analog circuit fault diagnosis model based on the DCQGA-SMKL-SVM method is constructed for analog circuit fault diagnosis. Tests on the bi-quadratic filter circuit and quad-amplifier second-order high-pass filter circuit show that compared with the DCQGA-SVM method, the proposed approach has higher fault diagnosis accuracy.