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Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

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arxiv 2101.10657 v3 pith:SETKEIL4 submitted 2021-01-26 quant-ph cs.AIcs.LG

Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

Daniela A. Zaidenberg , Alessandro Sebastianelli , Dario Spiller , Bertrand Le Saux , Silvia Liberata Ullo This is my paper
classification quant-ph cs.AIcs.LG
keywords quantumbottlenecksimageremotesensingadvantagesaimsalgorithms
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.

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    quant-ph 2026-04 unverdicted novelty 5.0

    Quantum feature maps from trained VQCs boost land-cover classification performance when reused in classical kernel-based frameworks, though linear-readout VQCs fail to surpass RBF-SVM baselines on EuroSAT-MS.