“Quantum annealing of the p-spin model under inhomogeneous transverse field driving”, [1808.01582], by Y. Susa, Y. Yamashiro, M. Yamamoto, I. Hen, D. A. Lidar and H. Nishimori

“Finite temperature quantum annealing solving exponentially small gap problem with non-monotonic success probability”, Nature Comm92917 (2018), by A. Mishra, T. Albash and D. A. Lidar [link]

“Demonstration of a Scaling Advantage for a Quantum Annealer over Simulated Annealing”, Phys. Rev. X 8, 031016 (2018), by T. Albash and D. A. Lidar [link]

“Test-driving 1000 qubits”, Quantum Science & Technology 3, 030501 (2018). Special issue on “What would you do with 1000 qubits” , by J. Job and D. A. Lidar [link]

“Quantum trajectories for time-dependent adiabatic master equations”, Phys. Rev. A 97, 022116 (2018), by K. W. Yip, T. Albash, D. A. Lidar [link]

“Quantum annealing versus classical machine learning applied to a simplified computational biology problem”, npj Quant. Info. 414 (2018), by R. Y. Li, R. Di Felice, R. Rohs and D. A. Lidar [link]

“Scalable effective temperature reduction for quantum annealers via nested quantum annealing correction”, Phys. Rev. A 97, 022308 (2018), by W. Vinci and D. A. Lidar [link]

“Adiabatic Quantum Computation”, Rev. Mod. Phys. 90, 015002 (2018), by T. Albash and D. A. Lidar [link]