Combinatorial Optimization with Quantum Computers

TL;DR

This paper introduces quantum optimization techniques, including quantum annealers and gate-based quantum computers, highlighting their potential advantages over classical methods for solving complex optimization problems.

Contribution

It provides a practical overview of quantum optimization methods, focusing on the use of quantum annealers and gate-based machines for solving optimization problems.

Findings

Quantum computers can potentially speed up optimization tasks.

Quantum annealers are specialized machines designed for optimization.

The paper offers an accessible introduction to quantum optimization techniques.

Abstract

Quantum computers leverage the principles of quantum mechanics to do computation with a potential advantage over classical computers. While a single classical computer transforms one particular binary input into an output after applying one operator to the input, a quantum computer can apply the operator to a superposition of binary strings to provide a superposition of binary outputs, doing computation apparently in parallel. This feature allows quantum computers to speed up the computation compared to classical algorithms. Unsurprisingly, quantum algorithms have been proposed to solve optimization problems in quantum computers. Furthermore, a family of quantum machines called quantum annealers are specially designed to solve optimization problems. In this paper, we provide an introduction to quantum optimization from a practical point of view. We introduce the reader to the use of quantum annealers and quantum gate-based machines to solve optimization problems.