In this article, we explore the potential of quantum machine learning for subsurface feature extractions from radar sounder signals. We propose a hybrid quantum-classical learning paradigm that leverages parameterized quantum circuits to generate probability amplitudes based on quantum properties such as superposition and entanglement. These amplitudes are synergistically integrated with the classical deep neural networks that are efficient in learning high-dimension contextual features for downstream prediction tasks. The present research work is structured around two objectives. First, we investigate the role of quantum circuits in the latent space for transferring back-and-forth rich discriminative spatial context from the encoder to the decoder for segmentation. Second, we investigate how the probabilistic amplitudes derived from quantum circuits are significant in integrating into the classical models to provide new insights for radar sounder signals segmentation. The performance of the hybrid architectures has been studied in small-scale settings by simulating the expected behaviour of the quantum circuits on a classical machine. The experimental results have demonstrated the viability of quantum machine learning frameworks on MCoRDS-1 and MCoRDS-3 datasets for radar sounder signal segmentation. Qualitatively, they are capable of delineating the spatial extent of the bedrock from noise. Additionally, we conduct a comparative analysis between theQiskit Aer Simulatorand theIBM FakeBackend Simulatorto highlight the computational trade-offs and validate fidelity of two simulators for scalable experimentation. Therefore, our work opens up new avenues of research for future radar sounder data analysis leading to more precise and efficient subsurface target segmentation.

The trend of building larger and more complex imaging satellite constellations leads to the challenge in managing multiple acquisition requests of the Earth surface. Optimally planning these acquisitions is an intractable optimization problem, and heuristic algorithms are used today for finding sub-optimal solutions. Recently, quantum algorithms have been considered for this purpose, due to the potential breakthroughs that they can bring in optimization, expecting either a speedup or an increase in the solution quality. Hybrid quantum-classical methods have been considered as a short-term solution for taking advantage of small quantum machines. In this paper, we propose reverse quantum annealing as a method for improving the acquisition plan obtained by a classical optimizer. We investigate the benefits of the method with different annealing schedules and different problem sizes. The obtained results provide guidelines on designing a larger hybrid quantum-classical framework based on reverse quantum annealing for this application.

This article presents a hybrid quantum-classical framework by incorporating quantum feature maps regulated classical Convolutional Neural Network (CNN) architecture in the context of detecting different subsurface targets in the radar sounder signal. The quantum feature maps are generated by quantum circuits to utilize spatially-bound input information from the input training samples. The associated spectral probabilistic amplitudes of the feature maps are further fed as an input to the classical CNN-based network to classify the subsurface targets in the radargram. Experimental results on the MCoRDS and MCoRDS3 dataset demonstrated the capability of contextualizing the classical architecture through quantum feature maps for characterizing the radar sounder data.

Regression analysis has a crucial role in many Earth Ob-servation (EO) applications. The increasing availability and recent development of new computing technologies moti-vate further research to expand the capabilities and enhance the performance of data analysis algorithms. In this paper, the biophysical variable estimation problem is addressed. A novel approach is proposed, which consists in a reformulated Support Vector Regression (SVR) and leverages Quantum Annealing (QA). In particular, the SVR optimization prob-lem is reframed to a Quadratic Unconstrained Binary Opti-mization (QUBO) problem. The algorithm is then tested on the D-Wave Advantage quantum annealer. The experiments presented in this paper show good results, despite current hardware limitations, suggesting that this approach is viable and has great potential.

Recent developments in Quantum Computing (QC) have paved the way for an enhancement of computing capabilities. Quantum Machine Learning (QML) aims at developing Machine Learning (ML) models specifically designed for quantum computers. The availability of the first quantum processors enabled further research, in particular the exploration of possible practical applications of QML algorithms. In this work, quantum formulations of the Support Vector Machine (SVM) are presented. Then, their implementation using existing quantum technologies is discussed and Remote Sensing (RS) image classification is considered for evaluation.