Boosting Quantum Annealing Performance Using Evolution Strategies for Annealing Offsets Tuning

In this paper we introduce a novel algorithm to iteratively tune annealing offsets for qubits in a D-Wave 2000Q quantum processing unit (QPU). Using a (1+1)-CMA-ES algorithm, we are able to improve the performance of the QPU by up to a factor of 12.4 in probability of obtaining ground states for small problems, and obtain previously inaccessible (i.e., better) solutions for larger problems. We also make efficient use of QPU samples as a resource, using 100 times less resources than existing tuning methods. The success of this approach demonstrates how quantum computing can benefit from classical algorithms, and opens the door to new hybrid methods of computing.

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