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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Veröffentlicht in:Quantum information processing Jg. 19; H. 7
Hauptverfasser: Willsch, Madita, Willsch, Dennis, Jin, Fengping, De Raedt, Hans, Michielsen, Kristel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2020
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1570-0755
1573-1332
DOI:10.1007/s11128-020-02692-8