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“Limitations of error corrected quantum annealing in improving the performance of Boltzmann machines”, Quantum Science and Technology **5**, 045010 (2020), by R. Li, T. Albash and D. A. Lidar [link]

“Fast, Lifetime-Preserving Readout for High-Coherence Quantum Annealers” [2006.10817] by J. A. Grover, J. I. Basham, A. Marakov, S. M. Disseler, R. T. Hinkey, M. Khalil, Z. A. Stegen, T. Chamberlin, W. DeGottardi, D. J. Clarke, J. R. Medford, J. D. Strand, M. Stoutimore, S. Novikov, D. G. Ferguson, D. A. Lidar, K. M. Zick, A. J. Przybysz

“Why and when is pausing beneficial in quantum annealing?”, *Phys. Rev. Applied* **14**, 014100 (2020), by H. Chen and D. A. Lidar [link]

“Anneal-path correction in flux qubits”, [2002.11217], by M. Khezri, J. Grover, J. Basham, S. Disseler, H. Chen, S. Novikov, K. Zick, D. A. Lidar

“Probing the Universality of Topological Defect Formation in a Quantum Annealer: Kibble-Zurek Mechanism and Beyond”, Phys. Rev. Research **2**, 033369 (September 2020), by Y. Bando, Y. Susa, H. Oshiyama, N. Shibata, M. Ohzeki, F. J. G´omez-Ruiz, D. A. Lidar, A. del Campo, S. Suzuki, and H. Nishimori [link]

“Unconventional machine learning of genome-wide human cancer data”, [1909.06206], by R. Li, S. Gujja, S. Bajaj, O. Gamel, N. Cilfone, J. Gulcher, D. A. Lidar and T. Chittenden

“Arbitrary-Time Error Suppression for Markovian Adiabatic Quantum Computing Using Stabilizer Subspace Codes”, Phys. Rev. A 100, 022326 (2019), by D. A. Lidar [link]

“Quantum adiabatic machine learning with zooming”, [1908.04480], by A. Zlokapa, A. Mott, J-R. Vlimant, J. Job, D. Lidar and M. Spiropulu

“Charged particle tracking with quantum annealing-inspired optimization”, [1908.04475], by A. Zlokapa, A. Anand, J-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu

“Analog Errors in Quantum Annealing: Doom and Hope” npj Quantum Information **5**, 107 (2019), by A. Pearson, A. Mishra, I. Hen and D. A. Lidar [link]

“Dynamics of reverse annealing for the fully-connected p-spin model”, Phys. Rev. A **100**, 052321 (2019) by Y. Yamashiro, M. Ohkuwa, H. Nishimori and D. A. Lidar [link]

“Nested Quantum Annealing Correction at Finite Temperature: p-spin models”, Phys. Rev. A **99**, 062307 (2019), by S. Matsuura, H. Nishimori, W. Vinci, D. A. Lidar [link]

“A Double-Slit Proposal for Quantum Annealing”, npj Quantum Information **5, **2 (2019), by H. Munoz-Bauza, H. Chen, D. A. Lidar [link]

“On the computational complexity of curing non-stoquastic Hamiltonians”, Nature Comm. **10**, 1571 (2019), by M. Marvian, D. A. Lidar and I. Hen [link]

“Sensitivity of quantum speedup by quantum annealing to a noisy oracle”, Phys. Rev. A **99**, 032324 (2019), by S. Muthukrishnan, T. Albash 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]

“Exploring More-Coherent Quantum Annealing”, 2018 IEEE International Conference on Rebooting Computing (ICRC), 1-7 (2018), by S. Novikov, R. Hinkey, S. Disseler, J. I. Basham, T. Albash, A. Risinger, D. Ferguson, D. A. Lidar and K. M. Zick [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]

“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]