量子深度学习在目标检测领域的应用

A Comprehensive Review of Quantum Deep Learning Application in Object Detection

量子深度学习以其并行运算优势而被引入计算机视觉,以改进目标检测任务。本文首先介绍了量子深度学习基本原理及复杂神经网络结构,概述其在图像处理、缺陷检测和自动驾驶领域的技术贡献。同时,本文指出了量子深度学习在目标检测领域目前面临的挑战,包括量子硬件资源限制、存在量子噪声、大规模数据集不足等。通过比较量子深度学习与传统深度学习的不足,本文提出了多模态融合、目标识别与分类及医学图像分析等未来发展方向,为相关领域研究提供了新的思路。

Quantum deep learning,integrating quantum computing and deep learning,has been introduced to enhance object 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 resources,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 directions,including multimodal fusion,object recognition and classification,and medical image analysis,which can provide new ideas and directions for scholars in associated fields.