A Comprehensive Review of Quantum Deep Learning Application in Object Detection
Quantum deep learning,integrating quantum computing and deep learning,has been introduced to enhance ob-ject detection tasks through its parallel computation advantages.This article begins by detailing the fundamental principles and intricate neural network architectures of quantum deep learning.It then outlines the technical contributions of quantum deep learning across image processing,defect detection,and autonomous driving.At the same time,it highlights several challenges faced by quantum deep learning within the realm of object detection.Challenges encompass limited quantum hardware re-sources,quantum noise,scarcity of large-scale datasets,as well as concerns regarding interpretability and debug ability.By comparing the limitations of quantum deep learning with traditional deep learning,the article proposes future development di-rections,including multimodal fusion,object recognition and classification,and medical image analysis,which can provide new ideas and directions for scholars in associated fields.
quantum deep learningobject detectionquantum computingneural networks
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维普
