Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

“…Three types of circuits, selected among the possible quantum circuits and to be used in the proposed hybrid QCNN, are presented. Their structure reflects the adopted implementation with 4 qubits, which represents a more complex architecture with respect to simpler ones where less qubits are used [26]. Far from being an exhaustive comparison of all possible quantum configurations, the description of the adopted circuits will allow to get an insight on how their…”

“…No entanglement circuit In the simple QCNN presented in [26], there is no entanglement and classical nodes are merely replaced by a parameters quantum node [42]. As seen in Fig.…”

“…Quantum circuit-based neural network classifiers for multi-spectral land cover classification have been introduced in preliminary proofof-concept applications as presented in [23], and an ensemble of support vector machines running on the D-Wave quantum annealer has been proposed for remote sensing image classification in [24]. Finally, in our preliminary work [25], available as Preprint at [26], hybrid quantum-classical neural networks for remote sensing applications are discussed, and a proof-ofconcept for binary classification, using multispectral optical data, is reported.…”

“…• QC is applied to land-cover classification on the reference benchmark EuroSAT dataset [27] for optical multispectral images, thus by going further than initial proofs-ofconcept on a few images [23], [24]. • QCNN multiclass classification is tackled, with respect to the simple binary classification already discussed in [26], and better results are obtained through the quantum-based networks with respect to their fully-classical counterpart. • A comparative and critical analysis is carried out to analyze the performances of different gate-based circuits for hybrid QCNN, showing advantages to the architecture with entanglement.…”