Quantum annealing
“Optimizing for periodicity: a model-independent approach to flux crosstalk calibration for superconducting circuits”, Quantum Science and Technology 9 025007 (2024), by X. Dai, R. Trappen, R. Yang, S. M. Disseler, J. I. Basham, J. Gibson, A. J. Melville, B. M. Niedzielski, R. Das, D. K. Kim, J. L. Yoder, S. J. Weber, C. F. Hirjibehedin, D. A. Lidar, A. Lupascu. [link]
“Scaling Advantage in Approximate Optimization with Quantum Annealing”, [2401.07184] by H. M. Bauza, D. A. Lidar.
“Demonstration of Error-Suppressed Quantum Annealing Via Boundary Cancellation”, Phys. Rev. Applied 19 , 034095 (2023), by H. Munoz-Bauza, L. Campos Venuti, and D. A. Lidar [link]
“Quantum adiabatic theorem for unbounded Hamiltonians with a cutoff and its application to superconducting circuits”, Phil. Trans. R. Soc. 381: 20210407 (2023), by E. Mozgunov and D. A. Lidar [link]
“Coherent quantum annealing in a programmable 2,000 qubit Ising chain”, Nat. Phys. (2022), by A. D. King, S. Suzuki, J. Raymond, A. Zucca, T. Lanting, F. Altomare, A. J. Berkley, S. Ejtemaee, E. Hoskinson, S. Huang, E. Ladizinsky, A. J. R. MacDonald, G. Marsden, T. Oh, G. Poulin-Lamarre, M. Reis, C. Rich, Y. Sato, J. D. Whittaker, J. Yao, R. Harris, D. A. Lidar, H. Nishimori, M. H. Amin [link]
“Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain”, npj Quantum Information 8, 85 (2021), by D. M. Tennant, X. Dai, A. J. Martinez, R. Trappen, D. Melanson, M. A. Yurtalan, Y. Tang, S. Bedkihal, R. Yang, S. Novikov, J. A. Grover, S. M. Disseler, J. I. Basham, R. Das, D. K. Kim, A. J. Melville, B. M. Niedzielski, S. J. Weber, J. L. Yoder, A. J. Kerman, E. Mozgunov, D. A. Lidar & A. Lupascu [link]
“Breakdown of the weak coupling limit in quantum annealing”, Phys. Rev. Applied 17, 054033 (2022), by Y. Bando, Ka-Wa Yip, H. Chen, D. A. Lidar, H. Nishimori [link]
“HOQST: Hamiltonian Open Quantum System Toolkit”, Communications Physics (2022)5:11, by H. Chen and D. A. Lidar [link]
“Customized quantum annealing schedules”, Phys. Rev. Applied 17, 044005 (2022), by M. Khezri, X. Dai, R. Yang, T. Albash, A. Lupascu, D. A. Lidar [link]
“Standard quantum annealing outperforms adiabatic reverse annealing with decoherence”, Phys. Rev. A 105, 032431 (2022), by G. Passarelli, K.-W. Yip, D.A. Lidar, P. Lucignano [link]
“3-Regular 3-XORSAT Planted Solutions Benchmark of Classical and Quantum Heuristic Optimizers”, Quantum Sci. Technol. 7 025008 (2022), by M. Kowalsky, T. Albash, I. Hen, D. A. Lidar [link]
“Optimal Control for Closed and Open System Quantum Optimization”, Phys. Rev. Applied 16, 054023 (2021), by L. Campos Venuti, D. D’Alessandro and D. A. Lidar [link]
“Charged particle tracking with quantum annealing-inspired optimization”, Quantum Machine Intelligence 3, 27 (2021), by A. Zlokapa, A. Anand, J-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu [link]
“Identification of driver genes for severe forms of COVID-19 in a deeply phenotyped young patient cohort”, Science Translational Medicine (2021), by
“Calibration of flux crosstalk in large-scale flux-tunable superconducting quantum circuits”, PRX Quantum 2, 040313 (2021), by X. Dai, D. M. Tennant, R. Trappen, A. J. Martinez, D. Melanson, M. A. Yurtalan, Y. Tang, S. Novikov, J. A. Grover, S. M. Disseler, J. I. Basham, R. Das, D. K. Kim, A. J. Melville, B. M. Niedzielski, S. J. Weber, J. L. Yoder, D. A. Lidar, and A. Lupascu [link]
“Phase transitions in the frustrated Ising ladder with stoquastic and non-stoquastic catalysts”, Phys. Rev. Research 3 (2021), by K. Takada, S. Sota, S. Yunoki, B. Pokharel, H. Nishimori, D. A. Lidar [link]
“Prospects for quantum enhancement with diabatic quantum annealing”, Nat Rev Phys 3, 466–489 (2021), by E. J. Crosson and D. A. Lidar [link]
“Quantum processor-inspired machine learning in the biomedical sciences”, Patterns 2, 100246 (2021) by R. Li, S. Gujja, S. Bajaj, O. Gamel, N. Cilfone, J. Gulcher, D. A. Lidar and T. Chittenden [link]
“Anneal-path correction in flux qubits”, npj Quantum Information 7, 36 (2021), by M. Khezri, J. Grover, J. Basham, S. Disseler, H. Chen, S. Novikov, K. Zick, D. A. Lidar [link]
“Quantum adiabatic machine learning with zooming”, Phys. Rev. A 102, 062405, by A. Zlokapa, A. Mott, J-R. Vlimant, J. Job, D. A. Lidar and M. Spiropulu [link]
“Fast, Lifetime-Preserving Readout for High-Coherence Quantum Annealers”, PRX Quantum 1, 020314, 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 [link]
“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]
“Why and when is pausing beneficial in quantum annealing?”, Phys. Rev. Applied 14, 014100 (2020), by H. Chen and D. A. Lidar [link]
“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]
“Arbitrary-Time Error Suppression for Markovian Adiabatic Quantum Computing Using Stabilizer Subspace Codes”, Phys. Rev. A 100, 022326 (2019), by D. A. Lidar [link]
“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]
