基于量子深度学习的遥感图像处理研究进展
Research Progress of Remote Sensing Image Processing Based on Quantum Deep Learning
DOI: 10.12677/csa.2026.167235, PDF,   
作者: 徐若恒：桂林理工大学物理与电子信息工程学院，广西 桂林
关键词: 量子深度学习；遥感图像；量子计算；Quantum Deep Learning； Remote Sensing Imagery； Quantum Computing
摘要: 本文系统综述了混合量子–经典计算架构在遥感图像处理中的应用现状，探索量子计算突破传统深度学习参数与算力瓶颈的理论潜力。梳理了含噪声中等规模量子(NISQ)时代下，量子深度学习(QDL)在图像分类与特征提取、高维光谱数据降维、小样本生成等任务中的研究进展，并剖析了多种量子数据编码策略的机制。对比现有文献发现，该交叉领域仍受制于数据编码效率、硬件退相干与梯度消失(贫瘠高原)、异构协同融合缺失等底层局限。针对上述制约，前瞻性探讨了量子迁移学习、张量网络、量子架构搜索(QNAS)等新兴技术在提升模型抗噪性与特征表征能力方面的演进趋势。结论指出，软硬件协同优化与误差缓解算法的深化，有望使量子深度学习成为多模态地球观测数据智能化解译中一种具备参数效能潜力的补充计算范式。
Abstract: This paper presents a systematic review of the current state of hybrid quantum classical computing architectures for remote sensing image processing, aiming to explore the theoretical potential of quantum computing to overcome the constraints of parameter explosion and computational cost inherent in traditional deep learning. In the Noisy Intermediate Scale Quantum (NISQ) era, the review summarizes research progress in quantum deep learning (QDL) for core tasks including image classification and feature extraction, dimensionality reduction of high dimensional spectral data, and few shot data generation. It also analyzes the underlying mechanisms of various quantum data encoding strategies. A comparative analysis of existing literature reveals that this interdisciplinary field is still hindered by fundamental limitations such as data encoding inefficiency for high resolution inputs, hardware decoherence and vanishing gradients (the barren plateau phenomenon), and the lack of effective heterogeneous synergy mechanisms. In response to these constraints, this paper prospectively discusses emerging technologies (quantum transfer learning, tensor networks, and quantum architecture search (QNAS)) and their evolutionary trends in enhancing model robustness and feature representation capability. The conclusion indicates that deeper integration of software hardware co optimization and error mitigation algorithms may enable quantum deep learning to serve as a complementary computing paradigm with parameter efficiency for the intelligent interpretation of multi modal Earth observation data in the future.
文章引用：徐若恒. 基于量子深度学习的遥感图像处理研究进展[J]. 计算机科学与应用, 2026, 16(7): 1-8. https://doi.org/10.12677/csa.2026.167235

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