QUANTUM-ASSISTED GREEDY ALGORITHMS

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.

Automated summary

This paper demonstrates how to improve candidate selection in greedy algorithms by leveraging quantum annealers (QAs). By sampling from the ground state of a problem-dependent Hamiltonian using QAs and estimating the probability distribution of problem variables, the proposed quantum-assisted greedy algorithm (QAGA) scheme outperforms state-of-the-art techniques in quantum annealing.