The blend of deep learning and quantum computing technologies might significantly improve AGI. Presented in this study is the revolutionary approach of QIDLF, or a framework using quantum-powered feature extraction to overcome the limitations posed by the computational techniques conventionally employed for processing deep neural networks. Quantum phenomenon—superposition and entanglement—simultaneous processing of numerous feature spaces—end. The framework integrates quantum circuits into the feature extraction layers of deep learning models, making it possible for them to discover complex, non-linear patterns in high-dimensional data, which are often missed by traditional algorithms. These quantum circuits operating in hybrid quantum classical systems accelerate key processes such as characteristic selection, reducing the dimension, and optimization; thereby the performance of the model will increase. Besides, QIDLF is quite resilient in noisy situations and therefore suitable for practical applications of AGI. This work establishes quantum-enhanced deep learning as a potential method for solving intricate, multifaceted problems, which marks a great step toward more effective and flexible intelligent systems. Our findings provide a new invention of research into the combination of “Quantum Computing” with “AGI”. Moreover, our results are contributions to the emerging area of quantum artificial intelligence.