Quantum annealing

“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]

“Exploring More-Coherent Quantum Annealing”, [1809.04485], by S. Novikov, R. Hinkey, S. Disseler, J. I. Basham, T. Albash, A. Risinger, D. Ferguson, D. A. Lidar and K. M. Zick

“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 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]

“Nested Quantum Annealing Correction at Finite Temperature: p-spin models”, [1803.01492], by S. Matsuura, H. Nishimori, W. Vinci, D. A. Lidar

“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]

“On the Computational Complexity of Curing the Sign Problem”, [1802.03408], by M. Marvian, D. A. Lidar and I. Hen

“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]

“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

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

“Optimally Stopped Optimization”, Phys. Rev. Applied 6, 054016 (2016), by W. Vinci and D. A. Lidar [link]

“Eigenstate Tracking in Open Quantum Systems”, Phys. Rev. A 94, 042131 (2016), by J. Jing, M. S. Sarandy, D. A. Lidar, D. W. Luo, and L. A. Wu [link]

“Simulated Quantum Annealing with Two All-to-All Connectivity Schemes”, Phys. Rev. A 94, 022327 (2016), by T. Albash, W. Vinci, and D. A. Lidar [link]

“Nested Quantum Annealing Correction”, Nature Quant. Info. 2, 16017 (2016), by W. Vinci, T. Albash, and D. A. Lidar [link]

“Tunneling and speedup in quantum optimization for permutation-symmetric problems”, Phys. Rev. X, 6, 031010 (2016), by S. Muthukrishnan, T. Albash, and D. A. Lidar [link]

“Mean Field Analysis of Quantum Annealing Correction”, Phys. Rev. Lett. 116, 220501 (2016), by S. Matsuura, H. Nishimori, T. Albash, D.A. Lidar [link]

“Performance of two different quantum annealing correction codes”, Quant. Info. Proc. 15, 2, pp. 609 (2016), by A. Mishra, T. Albash and D.A. Lidar [link]

“Reexamination of the evidence for entanglement in the D-Wave processor”, Phys. Rev. A 92, 062328 (2015) , by T. Albash, I. Hen, F. M. Spedalieri, D. A. Lidar [link]

“When Diabatic Trumps Adiabatic in Quantum Optimization”, [1505.01249], by S. Muthukrishnan, T. Albash, and D.A. Lidar

“Probing for quantum speedup in spin glass problems with planted solutions”, Phys. Rev. A 92, 042325 (2015), by I. Hen, J. Job, T. Albash, T.F. Ronnow, M. Troyer, and D.A. Lidar [link]

“Quantum Annealing Correction with Minor Embedding”,Phys. Rev. A 92, 042310 (2015), by W. Vinci, T. Albash, G. Paz-Silva, I. Hen, and D. A. Lidar [link]

“Consistency tests of classical and quantum models for a quantum annealer”, Phys. Rev. A 91, 042314 (2015), by T. Albash, W. Vinci, A. Mishra, P.A. Warburton, and D.A. Lidar [link]

“Quantum Annealing Correction for Random Ising Problems”, Phys. Rev. A 91, 042302 (2015), by K. Pudenz, T. Albash, and D. Lidar. [link]

“Decoherence in adiabatic quantum computation”, Phys. Rev. A 91, 062320 (2015), by T. Albash and D.A. Lidar [link]

“Reexamining classical and quantum models for the D-Wave One processor”, The European Physics Journal, Special Topics 224, 111 (special issue on quantum annealing) (2015), by T. Albash, T. Ronnow, M. Troyer, D.A. Lidar [link]

“Defining and Detecting Quantum Speedup”, Science 345, 420 (2014), by T.F. Ronnow, Z. Wang, J. Job, S.V. Isakov, D. Wecker, J.M. Martinis, D.A. Lidar, and M. Troyer.[link]

“MAX 2-SAT with up to 108 Qubits”, New J. Phys. 16, 045006 (2014), by S. Santra, G. Quiroz, G. Ver Steeg, and D.A. Lidar. [link]

“Error Corrected Quantum Annealing with Hundreds of Qubits”, Nature Communications 5, 3243 (2014), by K.P. Pudenz, T. Albash, and D.A. Lidar. [link][sup-mat]

“Evidence for Quantum Annealing with More Than One Hundred Qubits”, Nature Physics 10, 218 (2014), by S. Boixo, T. Ronnow, S. Isakov, Z. Wang, D. Wecker, D.A. Lidar, J. Martinis, and M. Troyer. [link][sup-mat]

“Adiabatic Quantum Optimization with the Wrong Hamiltonian”, Phys. Rev. A 88, 062314 (2013), by K.C. Young, R. Blume-Kohout, D.A. Lidar. [link]

“Comment on: “Classical Signature of Quantum Annealing””, [1305.5837], by L. Wang, T. Ronnow, S. Boixo, S. Isakov, Z. Wang, D. Wecker, D. Lidar, J. Martinis, and M. Troyer.

“Experimental Signature of Programmable Quantum Annealing”, Nature Communications 4, 2067 (2013), by S. Boixo, T. Albash, F. Spedalieri, N. Chancellor, D.A. Lidar. [link]

“Quantum Adiabatic Machine Learning”, Quantum Info. Process. 12, 2027  (2013), by K. Pudenz and D.A. Lidar. [link]

“Quantum Adiabatic Markovian Master Equations”, New J. of Physics 14, 123016 (2012), by T. Albash, S. Boixo, D.A. Lidar, and P. Zanardi. [link]