The recent emergence of novel computational devices, such as quantum computers, neuromorphic co-processors and digital annealers presents new opportunities for hardware accelerated hybrid optimization algorithms. Unfortunately, demonstrations of unquestionable performance gains leveraging novel hardware platforms have faced significant obstacles. One key challenge is understanding the algorithmic properties that distinguish such devices from established optimization approaches. Through the careful design of contrived optimization tasks, this work provides new insights into the computation properties of quantum annealing and suggests that this model has an uncanny ability to avoid local minima and quickly identify the structure of high quality solutions. A meticulous comparison to a variety of algorithms spanning both complete and local search suggest that quantum annealing’s performance on the proposed optimization tasks is unique. This result provides new insights into the time scales and types of optimization problems where quantum annealing has the potential to provide notable performance gains over established optimization algorithms and prompts the development of hybrid algorithms that combine the best features of quantum annealing and state-of-the-art classical approaches.