Quantum speedup for combinatorial optimization with flat energy landscapes
Abstract
Designing quantum algorithms with a speedup over their classical analogs is a central challenge in quantum information science. Motivated by recent experimental observations of a superlinear quantum speedup in solving the Maximum Independent Set problem on certain unit-disk graph instances [Ebadi et al., Science 376, 6598 (2022)], we develop a theoretical framework to analyze the relative performance of the optimized quantum adiabatic algorithm and a broad class of classical Markov chain Monte Carlo algorithms. We outline conditions for the quantum adiabatic algorithm to achieve a quadratic speedup on hard problem instances featuring flat low-energy landscapes and provide example instances with either a quantum speedup or slowdown. We then introduce an additional local Hamiltonian with no sign problem to the optimized adiabatic algorithm to achieve a quadratic speedup over a wide class of classical simulated annealing, parallel tempering, and quantum Monte Carlo algorithms in solving these hard problem instances. Finally, we use this framework to analyze the experimental observations.
Cite
@article{arxiv.2306.13123,
title = {Quantum speedup for combinatorial optimization with flat energy landscapes},
author = {Madelyn Cain and Sambuddha Chattopadhyay and Jin-Guo Liu and Rhine Samajdar and Hannes Pichler and Mikhail D. Lukin},
journal= {arXiv preprint arXiv:2306.13123},
year = {2023}
}
Comments
9+27 pages, 5+8 figures