Quantum Machine Learning for Remote Sensing: Exploring potential and
  challenges
Artur Miroszewski Jakub Nalepa Bertrand Le Saux Jakub Mielczarek
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.