Quantum-Assisted Greedy Algorithms

TL;DR

This paper introduces a quantum-assisted greedy algorithm that leverages quantum annealers to improve candidate selection in greedy algorithms, demonstrating better solutions on a D-Wave quantum processor.

Contribution

It presents a novel quantum-assisted approach that uses quantum annealers for candidate selection, outperforming traditional heuristics in greedy algorithms.

Findings

QAGA finds solutions with lower energy values.

Empirical results show improved performance over classical methods.

Quantum annealers effectively estimate variable probabilities.

Abstract

We show how to leverage quantum annealers 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 quantum annealers that sample from the ground state(s) of a problem-dependent Ising 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, revealed that the proposed quantum-assisted greedy algorithm (QAGA) can find notably better solutions (i.e., samples with lower energy value), compared to the state-of-the-art techniques in the realm of quantum annealing.