We show how to leverage quantum annealers (QAs) to better select candidates
in greedy algorithms. Unlike conventional greedy algorithms that employ
problem-specific heuristics for making locally optimal choices at each stage,
we use QAs that sample from the ground state of problem-dependent Hamiltonians
at cryogenic temperatures and use retrieved samples to estimate the probability
distribution of problem variables. More specifically, we look at each spin of
the Ising model as a random variable and contract all problem variables whose
corresponding uncertainties are negligible. Our empirical results on a D-Wave
2000Q quantum processor demonstrate that the proposed quantum-assisted greedy
algorithm (QAGA) scheme can find notably better solutions compared to the
state-of-the-art techniques in the realm of quantum annealingComment: in Proceedings of the 2022 International Geoscience and Remote
Sensing Symposium (IGARSS