Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller, Bertrand Le Saux, Silvia Liberata Ullo
Abstract
This article aims to explore the potential of current approaches for quantum image classification in the context of remote sensing. After a brief outline of quantum computers and an analysis of the current bottlenecks, it shows for the first time experiments with quantum neural networks on a reference Earth observation (EO) dataset: EuroSAT. Moreover, it establishes the proof of concept of quantum computing for EO: the models trained and run on a quantum simulator are on par with classical ones. We make the open-source code available for further developments.
Topics & Concepts
Computer scienceQuantum computerContext (archaeology)QuantumCode (set theory)Source codeArtificial neural networkArtificial intelligenceTheoretical computer scienceProgramming languagePhysicsQuantum mechanicsSet (abstract data type)BiologyPaleontologyQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyComputability, Logic, AI Algorithms