Quantum-Assisted Greedy Algorithms
Abstract
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 annealing
Cite
@article{arxiv.2208.02042,
title = {Quantum-Assisted Greedy Algorithms},
author = {Ramin Ayanzadeh and John E Dorband and Milton Halem and Tim Finin},
journal= {arXiv preprint arXiv:2208.02042},
year = {2022}
}
Comments
in Proceedings of the 2022 International Geoscience and Remote Sensing Symposium (IGARSS)