探究量子神经网络在遥感中的关键设计，验证其性能与稳定性。
On the impact of key design aspects in simulated Hybrid Quantum Neural Networks for Earth Observation
- 对比不同量子库在混合模型中的训练效率与效果。
- 分析初始化值对传统与量子增强模型的敏感性差异。
- 验证量子注意力机制提升视觉变换器在遥感任务的表现。
量子计算为机器学习任务带来了新视角。将量子技术与经典深度学习架构结合已成为多个领域（如地球观测，EO）的研究热点。然而，现有EO领域的相关研究主要聚焦于卷积结构改进，忽略了若干关键问题。本文通过三个案例研究，系统探讨混合量子机器学习模型在EO任务中的基础设计因素，旨在为未来更精确的仿真及后NISQ时代研究奠定基础。首先，(1) 比较不同量子库在训练混合量子模型时的计算效率与有效性；其次，(2) 分析传统模型与量子增强模型对初始值（种子值）的稳定性与敏感性；最后，(3) 探究将量子电路集成到视觉变换器（ViTs）中所带来的性能提升，评估其在地球观测应用中的潜力。
原文摘要 · Abstract (English)
Quantum computing has introduced novel perspectives for tackling and improving machine learning tasks. Moreover, the integration of quantum technologies together with well-known deep learning (DL) architectures has emerged as a potential research trend gaining attraction across various domains, such as Earth Observation (EO) and many other research fields. However, prior related works in EO literature have mainly focused on convolutional architectural advancements, leaving several essential topics unexplored. Consequently, this research investigates through three cases of study fundamental aspects of hybrid quantum machine models for EO tasks aiming to provide a solid groundwork for future research studies towards more adequate simulations and looking at the post-NISQ era. More in detail, we firstly (1) investigate how different quantum libraries behave when training hybrid quantum models, assessing their computational efficiency and effectiveness. Secondly, (2) we analyze the stability/sensitivity to initialization values (i.e., seed values) in both traditional model and quantum-enhanced counterparts. Finally, (3) we explore the benefits of hybrid quantum attention-based models in EO applications, examining how integrating quantum circuits into ViTs can improve model performance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。