Benchmarking the quantum approximate optimization algorithm

The performance of the quantum approximate optimization algorithm is evaluated by using three different measures: the probability of finding the ground state, the energy expectation value, and a ratio closely related to the approximation ratio. The set of problem instances studied consists of weight...

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Vydané v:	Quantum information processing Ročník 19; číslo 7
Hlavní autori:	Willsch, Madita, Willsch, Dennis, Jin, Fengping, De Raedt, Hans, Michielsen, Kristel
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.07.2020 Springer Nature B.V
Predmet:	Algorithms Computer simulation Data Structures and Information Theory Ground state Ising model Mathematical Physics Optimization Optimization algorithms Physics Physics and Astronomy Quantum computers Quantum Computing Quantum Information Technology Quantum Physics Simulators Spintronics Quantum annealing Quantum computation Optimization problems QAOA
ISSN:	1570-0755, 1573-1332
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Abstract	The performance of the quantum approximate optimization algorithm is evaluated by using three different measures: the probability of finding the ground state, the energy expectation value, and a ratio closely related to the approximation ratio. The set of problem instances studied consists of weighted MaxCut problems and 2-satisfiability problems. The Ising model representations of the latter possess unique ground states and highly degenerate first excited states. The quantum approximate optimization algorithm is executed on quantum computer simulators and on the IBM Q Experience. Additionally, data obtained from the D-Wave 2000Q quantum annealer are used for comparison, and it is found that the D-Wave machine outperforms the quantum approximate optimization algorithm executed on a simulator. The overall performance of the quantum approximate optimization algorithm is found to strongly depend on the problem instance.
AbstractList	The performance of the quantum approximate optimization algorithm is evaluated by using three different measures: the probability of finding the ground state, the energy expectation value, and a ratio closely related to the approximation ratio. The set of problem instances studied consists of weighted MaxCut problems and 2-satisfiability problems. The Ising model representations of the latter possess unique ground states and highly degenerate first excited states. The quantum approximate optimization algorithm is executed on quantum computer simulators and on the IBM Q Experience. Additionally, data obtained from the D-Wave 2000Q quantum annealer are used for comparison, and it is found that the D-Wave machine outperforms the quantum approximate optimization algorithm executed on a simulator. The overall performance of the quantum approximate optimization algorithm is found to strongly depend on the problem instance.
ArticleNumber	197
Author	Willsch, Dennis Jin, Fengping De Raedt, Hans Willsch, Madita Michielsen, Kristel
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Snippet	The performance of the quantum approximate optimization algorithm is evaluated by using three different measures: the probability of finding the ground state,...
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SubjectTerms	Algorithms Computer simulation Data Structures and Information Theory Ground state Ising model Mathematical Physics Optimization Optimization algorithms Physics Physics and Astronomy Quantum computers Quantum Computing Quantum Information Technology Quantum Physics Simulators Spintronics
Title	Benchmarking the quantum approximate optimization algorithm
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