The industry of quantum technologies is rapidly expanding, offering promising
opportunities for various scientific domains. Among these emerging
technologies, Quantum Machine Learning (QML) has attracted considerable
attention due to its potential to revolutionize data processing and analysis.
In this paper, we investigate the application of QML in the field of remote
sensing. It is believed that QML can provide valuable insights for analysis of
data from space. We delve into the common beliefs surrounding the quantum
advantage in QML for remote sensing and highlight the open challenges that need
to be addressed. To shed light on the challenges, we conduct a study focused on
the problem of kernel value concentration, a phenomenon that adversely affects
the runtime of quantum computers. Our findings indicate that while this issue
negatively impacts quantum computer performance, it does not entirely negate
the potential quantum advantage in QML for remote sensing.

量子技术快速扩展，量子机器学习在遥感领域的应用引起了广泛关注，本文调查了量子机器学习在遥感领域的潜力，并探讨了其面临的挑战，通过研究核值集中问题发现，量子计算机的性能受到了一定的负面影响，但并不完全否定量子机器学习在遥感中的潜在优势。