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- W4312912553 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 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 proces-sor 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." @default.
- W4312912553 created "2023-01-05" @default.
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- W4312912553 date "2022-07-17" @default.
- W4312912553 modified "2023-09-23" @default.
- W4312912553 title "Quantum-Assisted Greedy Algorithms" @default.
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- W4312912553 doi "https://doi.org/10.1109/igarss46834.2022.9884795" @default.
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