Quantum-Assisted Greedy Algorithms

• URL: http://arxiv.org/abs/2208.02042v1
• Date: Wed, 3 Aug 2022 13:09:17 GMT
• Title: Quantum-Assisted Greedy Algorithms
• Authors: Ramin Ayanzadeh, John E Dorband, Milton Halem, Tim Finin
• Abstract summary: We show how to leverage quantum annealers (QAs) to better select candidates in greedy algorithms. We use QAs that sample from the ground state of problem-dependent Hamiltonians at cryogenic temperatures.
• Score: 1.5049442691806054
• License: http://creativecommons.org/licenses/by/4.0/
• 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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