A review on Quantum Approximate Optimization Algorithm and its variants

The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance a...

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Veröffentlicht in:Physics reports Jg. 1068; S. 1 - 66
Hauptverfasser: Blekos, Kostas, Brand, Dean, Ceschini, Andrea, Chou, Chiao-Hui, Li, Rui-Hao, Pandya, Komal, Summer, Alessandro
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 02.06.2024
Schlagworte:
Combinatorial optimization problems
NISQ algorithms
Quantum Approximate Optimization Algorithm (QAOA)
Quantum optimization
Variational Quantum Algorithms (VQAs)
Quantum Approximate Optimization Algorithm (QAOA)
Variational Quantum Algorithms (VQAs)
Combinatorial optimization problems
Quantum optimization
NISQ algorithms
ISSN:0370-1573, 1873-6270
Online-Zugang:Volltext
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Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios, its applicability across various problem instances, and considerations of hardware-specific challenges such as error susceptibility and noise resilience. Additionally, we conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm. We aim to provide insights into key questions about the algorithm, such as whether it can outperform classical algorithms and under what circumstances it should be used. Towards this goal, we offer specific practical points in a form of a short guide.
AbstractList The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios, its applicability across various problem instances, and considerations of hardware-specific challenges such as error susceptibility and noise resilience. Additionally, we conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm. We aim to provide insights into key questions about the algorithm, such as whether it can outperform classical algorithms and under what circumstances it should be used. Towards this goal, we offer specific practical points in a form of a short guide.
Author Pandya, Komal
Summer, Alessandro
Brand, Dean
Ceschini, Andrea
Li, Rui-Hao
Blekos, Kostas
Chou, Chiao-Hui
Author_xml – sequence: 1
  givenname: Kostas
  orcidid: 0000-0002-6777-2107
  surname: Blekos
  fullname: Blekos, Kostas
  email: mplekos@upatras.gr
  organization: Department of Physics, School of Natural Sciences, University of Patras, Patras 26504, Greece
– sequence: 2
  givenname: Dean
  surname: Brand
  fullname: Brand, Dean
  email: 26162083@sun.ac.za
  organization: Department of Physics, Stellenbosch University, Stellenbosch 7600, South Africa
– sequence: 3
  givenname: Andrea
  surname: Ceschini
  fullname: Ceschini, Andrea
  email: andrea.ceschini@uniroma1.it
  organization: Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome 00184, Italy
– sequence: 4
  givenname: Chiao-Hui
  surname: Chou
  fullname: Chou, Chiao-Hui
  email: r05525046@ntu.edu.tw
  organization: Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10617, Taiwan
– sequence: 5
  givenname: Rui-Hao
  surname: Li
  fullname: Li, Rui-Hao
  email: rxl527@case.edu
  organization: Department of Physics, Case Western Reserve University, Cleveland, OH 44106, USA
– sequence: 6
  givenname: Komal
  surname: Pandya
  fullname: Pandya, Komal
  email: komalben_2021cs17@iitp.ac.in
  organization: Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna 801106, India
– sequence: 7
  givenname: Alessandro
  surname: Summer
  fullname: Summer, Alessandro
  email: summera@tcd.ie
  organization: School of Physics, Trinity College Dublin, Dublin 2, Ireland
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Keywords Quantum Approximate Optimization Algorithm (QAOA)
Variational Quantum Algorithms (VQAs)
Combinatorial optimization problems
Quantum optimization
NISQ algorithms
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Snippet The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization...
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SubjectTerms Combinatorial optimization problems
NISQ algorithms
Quantum Approximate Optimization Algorithm (QAOA)
Quantum optimization
Variational Quantum Algorithms (VQAs)
Title A review on Quantum Approximate Optimization Algorithm and its variants
URI https://dx.doi.org/10.1016/j.physrep.2024.03.002
Volume 1068
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