Hybrid classic-quantum systems for the classification of satellite imagery

What is it about?

Hybrid classic-quantum systems are a way to utilize today's quantum hardware for machine learning. By running subroutines for pre- and post-processing on classic hardware, it is possible to overcome the limitations in terms of low capacity and short sequences of realizable operations. In this work, the potential use of quantum machine learning for remote sensing is demonstrated and several hybrid approaches are compared.

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Photo by ImaginEarth La Terre En Images on Unsplash

Photo by ImaginEarth La Terre En Images on Unsplash

Why is it important?

Quantum machine learning is a vastly growing research field due to advances in quantum hardware and the hope for a quantum advantage over classic approaches. If a computational speed-up compared to classic computing can be realized, quantum machine learning may become a new way to process the growing amount of remote sensing imagery.