Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS)

Quantum-Assisted Greedy Algorithms

, , , and

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 a 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 annealing

  • 283874 bytes

  • 2223084 bytes

greedy algorithms, optimization, quantum annealing, quantum computing



Downloads: 416 downloads

UMBC ebiquity