Publications
2018
“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]
