Quantum algorithms
“Simulating nonlinear optical processes on a superconducting quantum device”, [2406.13003] by Y. Shi, B. Evert, A. F. Brown, V. Tripathi, E. A. Sete, V. Geyko, Y. Cho, J. L DuBois, D. A. Lidar, I. Joseph, M. Reagor.
“Simulating Chemistry on Bosonic Quantum Devices”, [2404.10214] by R. Dutta, D. G. A. Cabral, N. Lyu, N. P. Vu, Y. Wang, B. Allen, X. Dan, R. G. Cortiñas, P. Khazaei, S. E. Smart, S. Nie, M. H. Devoret, D. A. Mazziotti, P. Narang, C. Wang, J. D. Whitfield, A. K. Wilson, H. P. Hendrickson, D. A. Lidar, F. Pérez-Bernal, L. F. Santos, S. Kais, E. Geva, V. S. Batista
“Better-than-classical Grover search via quantum error detection and suppression”, npj Quantum Information volume 10, 23 (2024), by B. Pokharel and D. A. Lidar. [link]
“Beyond unital noise in variational quantum algorithms: noise-induced barren plateaus and fixed points”, [2402.08721] by P. Singkanipa, D.A. Lidar.
“Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem”, [2401.07934] by P. Singkanipa, V. Kasatkin, Z. Zhou, G. Quiroz, D.A. Lidar.
“Demonstration of algorithmic quantum speedup”, Phys. Rev. Lett. 130, 210602 (2023) by B. Pokharel and D. A. Lidar [link]
“Boundaries of quantum supremacy via random circuit sampling”, npj Quantum Information, 9, 36 (2023), by A. Zlokapa, S. Boixo, and D. A. Lidar [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
“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]
“Low overhead universality and quantum supremacy using only Z-control”, Phys. Rev. Research 3, 033207 (2021), by B. Barch, R. Mohseninia and 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]
“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]
“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]
“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]
“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 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
“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]
“Optimally Stopped Optimization”, Phys. Rev. Applied 6, 054016 (2016), by W. Vinci 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]
“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]
“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]
“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]
“Analysis of Generalized Grover Quantum Search Algorithms Using Recursion Equations”, Phys. Rev. A 63, 012310 (2001), by E. Biham, O. Biham, D. Biron, M. Grassl, D.A. Lidar, and D. Shapira [pdf]
“Calculating the Thermal Rate Constant with Exponential Speedup on a Quantum Computer”, Phys. Rev. E 59, 2429 (1999), by D.A. Lidar and H. Wang [pdf]
“Grovers Quantum Search Algorithm for Arbitrary Initial Amplitude Distribution”, Phys. Rev. A 60, 2742 (1999), by E. Biham, O. Biham, D. Biron, M. Grassl, and D.A. Lidar [pdf]
“Simulating Ising Spin Glasses on a Quantum Computer”, Phys. Rev. E 56, 3661 (1997), , by D.A. Lidar and O. Biham [pdf]
“Accuracy Versus Run Time in an Adiabatic Quantum Search”, Phys. Rev. A 82, 052305 (2010), by A.T. Rezakhani, A.K. Pimachev, and D.A. Lidar. [link]
“Adiabatic Quantum Algorithm for Search Engine Ranking”, Phys. Rev. Lett. 108, 230506 (2012), by S. Garnerone, P. Zanardi, and D.A. Lidar [link][sup-mat]
“An Implementation of the Deutsch-Jozsa Algorithm on Molecular Vibronic Coherences Through Four-Wave Mixing: a Theoretical Study”, Chem. Phys. Lett. 360, 459 (2002), by Z. Bihary, D.R. Glenn, D.A. Lidar, and V.A. Apkarian [pdf]
“Classical Ising Model Test for Quantum Circuits”, New J. Physics 12, 075026 (2010), by J. Geraci and D.A. Lidar [link]
“Encoding a Qubit into Multilevel Subspaces”, New J. Phys. 8, 35 (2006), by M. Grace, C. Brif, H. Rabitz, I. Walmsley, R. Kosut, and D.A. Lidar [link]
“On the Exact Evaluation of Certain Instances of the Potts Partition Function by Quantum Computers”, Commun. Math. Phys279, 3, 735-768 (2008), by J. Geraci and D.A. Lidar [link]
“On the Quantum Computational Complexity of the Ising Spin Glass Partition Function and of Knot Invariants”, New J. Phys. 6, 167 (2004), by D. Lidar [pdf]
“Polynomial-Time Simulation of Pairing Models on a Quantum Computer”, Phys. Rev. Lett. 89, 057904 (2002), by L.-A. Wu, M.S. Byrd, and D.A. Lidar [pdf]
“Quantum Adiabatic Brachistochrone”, Phys. Rev. Lett. 103, 080502 (2009), by A.T. Rezakhani, W.J. Kuo, A. Hamma, D.A. Lidar, and P. Zanardi. [link]
“Quantum Adiabatic Machine Learning”, Quantum Info. Process. 12, 2027 (2013), by K. Pudenz and D.A. Lidar. [link]
“Quantum Malware”, Quant. Info. Processing 5, 69 (2006), by L.A. Wu and D.A. Lidar [link]
“Reply to: “Comment on `Polynomial-Time Simulation of Pairing Models on a Quantum Computer’””, Phys. Rev. Lett. 90, 249804 (2003), by L.-A. Wu, M.S. Byrd, and D.A. Lidar [pdf]
“Simple Proof of Equivalence Between Adiabatic Quantum Computation and the Circuit Model”, Phys. Rev. Lett. 99, 070502 (2007), by A. Mizel, D.A. Lidar, and M. Mitchell [link]
