Performance Improvement of a Quantum Annealer Using Optimized Quantum Control

Adiabatic quantum computation (AQC) relies on controlled adiabatic evolution to implement a quantum algorithm. Properly designed time-optimal control has been shown to be particularly advantageous for AQC. Grover's search algorithm is one such example where analytically-derived time-optimal control leads to improved scaling of the minimum energy gap between the ground state and first excited state and thus, the well-known quadratic quantum speedup. Recently, the D-Wave Systems quantum processing unit (QPU) -- a system designed to implement quantum annealing (a non-universal, finite-temperature version of AQC) -- has been upgraded with the ability to manipulate the annealing schedule; thus, enabling an evaluation of the effect of optimized control on computational accuracy. Here, we evaluate the new control features of the device for a range of optimization techniques, assessing the potential benefits of control for enhancing QPU performance for hard problem instances. Specifically, we employ closed-loop control optimization protocols based on stochastic gradient ascent and Bayesian optimization to optimize QPU performance. We focus on engineered hard problem instances for the QPU that exhibit small energy gaps and strong susceptibility to noise.