Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem
Abstract
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances. We perform noiseless simulations with up to 40 qubits and observe that the runtime of QAOA with fixed parameters scales better than branch-and-bound solvers, which are the state-of-the-art exact solvers for LABS. The combination of QAOA with quantum minimum finding gives the best empirical scaling of any algorithm for the LABS problem. We demonstrate experimental progress in executing QAOA for the LABS problem using an algorithm-specific error detection scheme on Quantinuum trapped-ion processors. Our results provide evidence for the utility of QAOA as an algorithmic component that enables quantum speedups.
Cite
@article{arxiv.2308.02342,
title = {Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem},
author = {Ruslan Shaydulin and Changhao Li and Shouvanik Chakrabarti and Matthew DeCross and Dylan Herman and Niraj Kumar and Jeffrey Larson and Danylo Lykov and Pierre Minssen and Yue Sun and Yuri Alexeev and Joan M. Dreiling and John P. Gaebler and Thomas M. Gatterman and Justin A. Gerber and Kevin Gilmore and Dan Gresh and Nathan Hewitt and Chandler V. Horst and Shaohan Hu and Jacob Johansen and Mitchell Matheny and Tanner Mengle and Michael Mills and Steven A. Moses and Brian Neyenhuis and Peter Siegfried and Romina Yalovetzky and Marco Pistoia},
journal= {arXiv preprint arXiv:2308.02342},
year = {2024}
}
Comments
Journal-accepted version