  The focus of this work is to explore the use of quantum annealing solvers for
the problem of phase unwrapping of synthetic aperture radar (SAR) images.
Although solutions to this problem exist based on network programming, these
techniques do not scale well to larger-sized images. Our approach involves
formulating the problem as a quadratic unconstrained binary optimization (QUBO)
problem, which can be solved using a quantum annealer. Given that present
embodiments of quantum annealers remain limited in the number of qubits they
possess, we decompose the problem into a set of subproblems that can be solved
individually. These individual solutions are close to optimal up to an integer
constant, with one constant per sub-image. In a second phase, these integer
constants are determined as a solution to yet another QUBO problem. We test our
approach with a variety of software-based QUBO solvers and on a variety of
images, both synthetic and real. Additionally, we experiment using D-Wave
Systems's quantum annealer, the D-Wave 2000Q. The software-based solvers obtain
high-quality solutions comparable to state-of-the-art phase-unwrapping solvers.
We are currently working on optimally mapping the problem onto the restricted
topology of the quantum annealer to improve the quality of the solution.
