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arXiv · 2208.02042

Quantum-Assisted Greedy Algorithms

Ramin Ayanzadeh·John E Dorband·Milton Halem·Tim Finin
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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

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Ramin Ayanzadeh, John E Dorband, Milton Halem, Tim Finin. 2022-08-03. Quantum-Assisted Greedy Algorithms. https://arxiv.org/abs/2208.02042

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