Quantum Machine Learning for Remote Sensing: Exploring potential and challenges

TL;DR

This paper explores the potential and challenges of applying Quantum Machine Learning to remote sensing data analysis, highlighting that quantum advantage is promising but hindered by kernel value concentration issues.

Contribution

It provides an analysis of quantum advantages in remote sensing and investigates the impact of kernel value concentration on quantum computer performance.

Findings

Kernel value concentration affects quantum computer runtime.

Quantum advantage remains possible despite challenges.

QML offers promising insights for remote sensing analysis.

Abstract

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.