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arXiv · 1907.02359

Benchmarking the Quantum Approximate Optimization Algorithm

Madita Willsch·Dennis Willsch·Fengping Jin·Hans De Raedt·Kristel Michielsen
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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 is 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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Madita Willsch, Dennis Willsch, Fengping Jin, Hans De Raedt, Kristel Michielsen. 2020-06-05. Benchmarking the Quantum Approximate Optimization Algorithm. https://doi.org/10.1007/s11128-020-02692-8

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