Potential of Quantum Machine Learning for Processing Multispectral Earth Observation Data

Quantum computers with hundreds of noisy qubits are already available for the research community. They have the potential to run complex quantum computations well beyond the computational capacity of any classical device. It is natural to ask the question, what application these devices could be useful for? Land Use and Land Cover classification of multispectral Earth observation data collected from the earth observation satellite mission is one such problem that is hard for classical methods due to its unique characteristics. In this work, we compare the performance of several classical machine learning algorithms on the stilted re-labeled dataset of the Copernicus Sentinel-2 mission, when the algorithm has access to Projected Quantum Kernel (PQK) features. We show that the classification accuracy increases drastically when the model has access to PQK features. We then naively study the performance of these algorithms with and without access to PQK features on the original Copernicus Sentinel-2 mission data set. This study provides key evidence that shows the potential of quantum machine learning methods for Earth Observation data.