An Enhanced Hybrid Approach Using D-Wave's CQM to Solve the Phase Unwrapping Problem

Quantum computers and algorithms are advancing rapidly, showing promise in tackling complex computational challenges. However, current quantum annealers are limited in handling large-scale problems due to their limited size and inherent noise. As a near-term solution, hybrid computing has emerged, harnessing the strengths of classical and quantum computing together. The phase unwrapping problem is a compu- tationally challenging task in interferometric synthetic aperture radar (In-SAR) image processing. In this study, we propose a hybrid approach to address the phase unwrapping problem for images larger than what current quantum annealers can handle. We utilized D-Wave's hybrid workflow, followed by refinements to enhance its performance. Our approach involves decomposing images and solving the resulting sub-images usingD- Wave's Constrained Quadratic Model (CQM), and then combining them. We assume that the obtained labels of sub-images are within an additive factor of the true labels. To determine these additive factors, we allow sub-images to overlap and use the labels of overlapping sectors for additive factor determination. Another phase of D- Wave's CQM is used for obtaining these shift factors. This enhanced CQM method results in improved outcomes compared to applying CQM to the entire image. Moreover, the proposed method enables unwrapping images larger than those that CQM can handle.