Prospects for quantum enhancement with diabatic quantum annealing

TLDR: In this paper, the authors assess the prospects for algorithms within the general framework of quantum annealing to achieve a quantum speedup relative to classical state-of-the-art methods.

Abstract: Optimization, sampling and machine learning are topics of broad interest that have inspired significant developments and new approaches in quantum computing. One such approach is quantum annealing (QA). In this Review, we assess the prospects for algorithms within the general framework of QA to achieve a quantum speedup relative to classical state-of-the-art methods. We argue for continued exploration in the QA framework on the basis that improved coherence times and control capabilities will enable the near-term exploration of several heuristic quantum optimization algorithms. These continuous-time Hamiltonian computation algorithms rely on control protocols that are more advanced than those in traditional ground-state QA, while still being considerably simpler than those used in gate-model implementations. The inclusion of coherent diabatic transitions to excited states results in a generalization we refer to collectively as diabatic quantum annealing, which we believe is the most promising route to quantum enhancement within this framework. Other promising variants of traditional QA include reverse annealing, continuous-time quantum walks and analogues of parameterized quantum circuit ansatzes for machine learning. Most of these algorithms have no known efficient classical simulations, making them worthy of further investigation with quantum hardware in the intermediate-scale regime. Quantum annealing is a widely used heuristic algorithm for optimization and sampling, implemented in commercial processors. This Review provides a critical assessment of the field and points to new opportunities for a quantum advantage via recently developed alternative quantum annealing protocols.