Challenges and opportunities in quantum optimization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00379058" target="_blank" >RIV/68407700:21230/24:00379058 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1038/s42254-024-00770-9" target="_blank" >https://doi.org/10.1038/s42254-024-00770-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s42254-024-00770-9" target="_blank" >10.1038/s42254-024-00770-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Challenges and opportunities in quantum optimization

Popis výsledku v původním jazyce

Quantum computers have demonstrable ability to solve problems at a scale beyond brute-force classical simulation. Interest in quantum algorithms has developed in many areas, particularly in relation to mathematical optimization - a broad field with links to computer science and physics. In this Review, we aim to give an overview of quantum optimization. Provably exact, provably approximate and heuristic settings are first explained using computational complexity theory, and we highlight where quantum advantage is possible in each context. Then, we outline the core building blocks for quantum optimization algorithms, define prominent problem classes and identify key open questions that should be addressed to advance the field. We underscore the importance of benchmarking by proposing clear metrics alongside suitable optimization problems, for appropriate comparisons with classical optimization techniques, and discuss next steps to accelerate progress towards quantum advantage in optimization. This Review discusses quantum optimization, focusing on the potential of exact, approximate and heuristic methods, core algorithmic building blocks, problem classes and benchmarking metrics. The challenges for quantum optimization are considered, and next steps are suggested for progress towards achieving quantum advantage. Quantum computing is advancing rapidly, and quantum optimization is a promising area of application. Quantum optimization algorithms - whether provably exact, provably approximate or heuristic - offer opportunities to demonstrate quantum advantage. Systematic benchmarking is crucial to guide research, track progress and further advance understanding of quantum optimization. Theoretical research and empirical research using real hardware can complement each other, in the move towards demonstrating quantum advantage.

Název v anglickém jazyce

Challenges and opportunities in quantum optimization

Popis výsledku anglicky

Quantum computers have demonstrable ability to solve problems at a scale beyond brute-force classical simulation. Interest in quantum algorithms has developed in many areas, particularly in relation to mathematical optimization - a broad field with links to computer science and physics. In this Review, we aim to give an overview of quantum optimization. Provably exact, provably approximate and heuristic settings are first explained using computational complexity theory, and we highlight where quantum advantage is possible in each context. Then, we outline the core building blocks for quantum optimization algorithms, define prominent problem classes and identify key open questions that should be addressed to advance the field. We underscore the importance of benchmarking by proposing clear metrics alongside suitable optimization problems, for appropriate comparisons with classical optimization techniques, and discuss next steps to accelerate progress towards quantum advantage in optimization. This Review discusses quantum optimization, focusing on the potential of exact, approximate and heuristic methods, core algorithmic building blocks, problem classes and benchmarking metrics. The challenges for quantum optimization are considered, and next steps are suggested for progress towards achieving quantum advantage. Quantum computing is advancing rapidly, and quantum optimization is a promising area of application. Quantum optimization algorithms - whether provably exact, provably approximate or heuristic - offer opportunities to demonstrate quantum advantage. Systematic benchmarking is crucial to guide research, track progress and further advance understanding of quantum optimization. Theoretical research and empirical research using real hardware can complement each other, in the move towards demonstrating quantum advantage.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA23-07947S" target="_blank" >GA23-07947S: Identifikace modelu kvantového systému jako problém nekomutativní polynomiální optimalizace</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Nature Reviews Physics

ISSN

2522-5820

e-ISSN

2522-5820

Svazek periodika

6

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

718-735

Kód UT WoS článku

001344204600001

EID výsledku v databázi Scopus

2-s2.0-85207968009