“Solving a Higgs optimization problem with quantum annealing for machine learning”, Nature 550, 375 (2017), A. Mott, J. Job, J. R. Vlimant, D. A. Lidar, and M. Spiropulu [link] 

“Non-stoquastic Hamiltonians in quantum annealing via geometric phases”, Nature Quant. Info. 3, 38 (2017), by W. Vinci and D. A. Lidar [link]

“Suppression of effective noise in Hamiltonian simulations”, Phys. Rev. A 96, 052328 (2017) , by M. Marvian, T. Brun and D. A. Lidar [link]

“Quasi-adiabatic Grover search via the WKB approximation”, Phys. Rev. A 96, 012329 (2017), by S. Muthukrishnan and D. A. Lidar [link]

“Relaxation vs. adiabatic quantum steady state preparation: which wins?”, Phys. Rev. A 95, 042302 (2017), by L. Campos Venuti, T. Albash, M. Marvian, D. A. Lidar, and P. Zanardi [link]

“Error Suppression for Hamiltonian Quantum Computing in Markovian Environments”, Phys. Rev. A 95, 032302 (2017), by M. Marvian and D. A. Lidar [link]

“Quantum annealing correction at finite temperature: ferromagnetic p-spin models”, Phys. Rev. A 95, 022308 (2017), by S. Matsuura, H. Nishimori, W. Vinci, T. Albash, and D. A. Lidar [link]

“Error suppression for Hamiltonian-based quantum computation using subsystem codes”, Phys. Rev. Lett. 118, 030504 (2017), by M. Marvian and D. A. Lidar [link]

“Evolution Prediction from Tomography”, Q. Info. Proc. 16(3), 1 (2017), by J. Dominy, L. Campos-Venuti, A. Shabani, and D.A. Lidar [link]