The problem of conserving computational resources while training models in deep learning has become urgent as models increasingly require more input data to improve accuracy. To enhance accuracy beyond increasing data, researchers have leveraged quantum properties, such as using random quantum circuits (RQC) to transform input data, similar to how data augmentation techniques are applied, and have demonstrated their effectiveness. However, the appearance of RQC introduces new challenges, as quantum circuits transforming features consume significant computational resources and increase the algorithm’s time complexity. In this study, we propose a novel framework combining RQC and saliency map techniques to address the computational resource problem in quantum deep learning. The research results show that our framework has reduced the algorithm’s computation time by 2.34 times, achieving an accuracy of 92.51%, 2.2% higher than the quantum baseline version. This reduction is highly significant in the current era, where quantum computers are in the noisy intermediate-scale quantum (NISQ) era, and noise significantly impacts the accuracy of hardware computation outputs, making quantum hardware accesses relatively costly.