“Demonstration of fidelity improvement using dynamical decoupling with superconducting qubits”, Phys. Rev. Lett. **121**, 220502 (2018), by B. Pokharel, N. Anand, B Fortman and D. A. Lidar [link]

“Quantum annealing of the p-spin model under inhomogeneous transverse field driving”, Phys. Rev. A **98**, 042326 (2018), by Y. Susa, Y. Yamashiro, M. Yamamoto, I. Hen, D. A. Lidar and H. Nishimori [link]

“Non-Markovianity of the Post Markovian Master Equation”, Phys. Rev. A **98**, 042119 (2018), by C. Sutherland, T. A. Brun and D. A. Lidar [link]

“Error Reduction in Quantum Annealing using Boundary Cancellation: Only the End Matters”, Phys. Rev. A **98**, 022315 (2018) , by L. Campos Venuti and D. A. Lidar [link]

“Reverse annealing for the fully connected p-spin model”, Phys. Rev. A **98**, 022314 (2018), by M. Ohkuwa, H. Nishimori and D. A. Lidar [link]

“Finite temperature quantum annealing solving exponentially small gap problem with non-monotonic success probability”, Nature Comm*. ***9**, 2917 (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. **4**, 14 (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]