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| 024 | 7 | _ | |2 doi |a 10.1007/s11128-020-02692-8 |
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| 100 | 1 | _ | |0 P:(DE-Juel1)167543 |a Willsch, Madita |b 0 |e Corresponding author |
| 245 | _ | _ | |a Benchmarking the quantum approximate optimization algorithm |
| 260 | _ | _ | |a Dordrecht |b Springer Science + Business Media B.V. |c 2020 |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |0 P:(DE-Juel1)144355 |a Jin, Fengping |b 2 |u fzj |
| 700 | 1 | _ | |0 P:(DE-Juel1)179169 |a De Raedt, Hans |b 3 |u fzj |
| 700 | 1 | _ | |0 P:(DE-Juel1)138295 |a Michielsen, Kristel |b 4 |u fzj |
| 773 | _ | _ | |0 PERI:(DE-600)2088114-9 |a 10.1007/s11128-020-02692-8 |g Vol. 19, no. 7, p. 197 |n 7 |p 197 |t Quantum information processing |v 19 |x 1570-0755 |y 2020 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/875239/files/Willsch2020_Article_BenchmarkingTheQuantumApproxim.pdf |y OpenAccess |
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