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

TL;DR

The novel QCNN proposed in this work is applied to the land-use and land-cover classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the reference benchmark.

Abstract

This article aims to investigate how circuit-based hybrid Quantum Convolutional Neural Networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case, and tested on the EuroSAT dataset used as reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for futures investigations.

Figures (12)

• Figure 1: Qubit modeling as hydrogen atom, with electron ground state $\ket{0}$ and first exited state $\ket{1}$.
• Figure 2: The Bloch sphere representing the probabilistic space in which the quantum state can exist. Gate operations rotate $\ket{\psi}$ about the Bloch sphere, changing the phase and the probability amplitudes of the qubit.
• Figure 4: Quantum circuit to create Bell state.
• Figure 5: Interface between classical and quantum layers.
• Figure 6: No entanglement circuit.