“An integralâ free representation of the Dyson series using divided differences”, New J. Phys. 23 103035(2021), by Amir Kalev and Itay Hen [link]

“Determining Quantum Monte Carlo Simulability with Geometric Phases”, Phys. Rev. Research 3, 023080(2021) , by Itay Hen [link]

"Quantum Algorithm for Simulating Hamiltonian Dynamics with an Off-diagonal Series Expansionâ" published at Quantum-Journal.org, by Amir Kalev and Itay Hen [link]

Itay Hen, invited to present his poster “Novel methods for simulating quantum many-body systems on classical and quantum computers” at the ACS (American Chemical Society) Spring 2021

 

Elizabeth Crosson, Tameem Albash, Itay Hen, and A. P. Young, De-Signing Hamiltonians for Quantum Adiabatic Optimization, Quantum 4 (2020), 334

Kelly Geyer, Anastasios Kyrillidis, and Amir Kalev, Low-rank regularization and solution uniqueness in over-parameterized matrix sensing, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (Silvia Chiappa and Roberto Calandra, eds.), Proceedings of Machine Learning Research, vol. 108, Proceedings of Machine Learning Research, 26–28 Aug 2020, pp. 930–94.

Zoe Gonzalez Izquierdo, Ruilin Zhou, Klas Markstr"om, and Itay Hen, Discriminating nonisomorphic graphs with an experimental quantum annealer, Phys. Rev. A 102 (2020), 032622.

Lalit Gupta, Tameem Albash, and Itay Hen, Permutation matrix representation quantum monte carlo, Journal of Statistical Mechanics: Theory and Experiment 2020 (2020), no. 7, 073105.

Lalit Gupta and Itay Hen, Elucidating the interplay between non-stoquasticity and the sign problem, Advanced Quantum Technologies 3 (2020), no. 1, 1900108.

Simulating hamiltonian dynamics with an off-diagonal series expansion [link]

Amir Kalev and Itay Hen, An integral-free representation of the dyson series using divided difference [link]

Joel Klassen, Milad Marvian, Stephen Piddock, Marios Ioannou, Itay Hen, and Barbara M.Terhal, Hardness and ease of curing the sign problem for two-local qubit hamiltonians, SIAM Journal on Computing 49 (2020), no. 6, 1332–136.

Jeremy Liu, Ke-Thia Yao, and Federico Spedalieri, Dynamic topology reconfiguration of boltzmann machines on quantum annealers, Entropy 22 (2020), no. 11.

Marco Paini, Amir Kalev, Dan Padilha, and Brendan Ruck, Estimating expectation values using approximate quantum states, 2020.

Hong Xu, Kexuan Sun, Sven Koenig, Itay Hen, and Satish Thittamaranahalli, Hybrid quantum-classical algorithms for solving the weighted csp, Proceedings of the Sixteenth International Symposium on Artificial Intelligence and Mathematics (ISAIM-2020) (2020)

Nicole Yunger Halpern, Michael E. Beverland, and Amir Kalev, Noncommuting conserved charges in quantum many-body thermalization, Physical Review E 101 (2020), no. 4.

 

T Albash and Itay Hen, Future of physical quantum annealers: impediments and hopes, Science and Culture 85 (2019), 163–170

Tameem Albash, Victor Martin-Mayor, and Itay Hen, Analog errors in ising machines, Quantum Science and Technology 4 (2019), no. 2, 02–03.

Lev Barash, Jeffrey Marshall, Martin Weigel, and Itay Hen, Estimating the density of states of frustrated spin systems, New Journal of Physics 21 (2019), no. 7, 073065.

Itay Hen, Equation planting: A tool for benchmarking ising machines, Phys. Rev. Applied 12 (2019), 011003

How quantum is the speedup in adiabatic unstructured search?, Quant. Inf. Proc. 18 (2019), 162

Resolution of the sign problem for a frustrated triplet of spins, Phys. Rev. E 99 (2019), 033306

Jeffrey Marshall, Davide Venturelli, Itay Hen, and Eleanor G. Rieffel, Power of pausing: Advancing understanding of thermalization in experimental quantum annealers, Phys. Rev. Applied 11 (2019), 044083.

Milad Marvian, Daniel A. Lidar, and Itay Hen, On the computational complexity of curing nonstoquastic hamiltonians, Nature Communications 10 (2019), no. 1, 1571.

Adam Pearson, Anurag Mishra, Itay Hen, and Daniel A. Lidar, Analog errors in quantum annealing: doom and hope, npj Quantum Information 5 (2019), no. 1, 107

Mikhail Slutskii, Tameem Albash, Lev Barash, and Itay Hen, Analog nature of quantum adiabatic unstructured search, New Journal of Physics 21 (2019), no. 11, 113025

 

“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]

“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]

 

"Off-diagonal expansion quantum Monte Carlo", Phys. Rev. E 96, 063309 (2017), by T. Albash, G. Wagenbreth and I. Hen [link]

"Temperature Scaling Law for Quantum Annealing Optimizers", Phys. Rev. Lett. 119, 110502 (2017), by T. Albash, V. Martin-Mayor and I. Hen [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 [link]

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

 

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

“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]

“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]

“Adiabaticity in open quantum systems”, Phys. Rev. A 93, 032118 (2016), by L.C. Venuti, T. Albash, D. A. Lidar, and P. Zanardi [link]

“Performance of two different quantum annealing correction codes”, Quant. Info. Proc. 15, 2, pp. 609-636 (2016), by A. Mishra, T. Albash, D.A. Lidar [link]

"Quantum versus simulated annealing in wireless interference network optimization", Nature Sci. Rep. 25797 (2016), by C. Wang, H. Chen, E. Jonckheere [link]

 

“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”, Journal of Physics: Conference Series 640, 012038 (2015), by W. Vinci, T. Albash, G. Paz-Silva, I. Hen, and D. A. Lidar [link]

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

“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 [pdf]

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

“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]

"Performance of the quantum adiabatic algorithm on constraint satisfaction and spin glass problems", European Physical Journal Special Topics 224, 63-73 (2015), by I. Hen and A. P. Young. [link]

"Quantum gates with controlled adiabatic evolutions", Phys. Rev. A 91, 022309 (2015), by I. Hen. [pdf]

"Unraveling Quantum Annealers using Classical Hardness", [1502.02494], by I. Hen and V. Martin-Mayor.

“Reexamination of the evidence for entanglement in the D-Wave processor”, [1506.03539], by T. Albash, I. Hen, F. M. Spedalieri, D. A. Lidar

 

“Quantum error suppression with commuting Hamiltonians: Two-local is too local”, Phys. Rev. Lett. 113, 260504 (2014), by I. Marvian and D.A. Lidar [pdf]

"Entanglement in a quantum annealing processor", Phys. Rev. X 4, 021041 (published 29 May 2014), by T. Lanting, A.J. Przybysz, A. Yu. Smirnov, F.M. Spedalieri, M.H. Amin, A.J. Berkley, R. Harris, F. Altomare, S. Boixo, P. Bunyk, N. Dickson, C. Enderud, J.P. Hilton, E. Hoskinson, M.W. Johnson, E. Ladizinsky, N. Ladizinsky, R. Neufeld, T. Oh, I. Perminov, C. Rich, M.C. Thom, E. Tolkacheva, S. Uchaikin, A.B. Wilson and G. Rose. [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]

“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]

"Evidence of 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.Lidar, J. Martinis, and M. Troyer. [pdf]

"Error-Corrected Quantum Annealing with Hundreds of Qubits", Nature Communications 5, 3243 (2014), by K. Pudenz, T. Albash, and D. Lidar. [pdf]

"How Fast Can Quantum Annealers Count?", J. Phys. A: Math. Theor. 47, 235304 (2014), by I. Hen [pdf]

"Continuous-Time Quantum Algorithms for Unstructured Problems", J. Phys. A: Math. Theor. 47, 045305 (2014), by I. Hen [pdf]

"Period finding with Adiabatic Quantum Computation", Europhysics Letters 105, 50005 (2014), by I. Hen [pdf]

"Phase Transitions in Planning Problems: Design and Analysis of Parameterized Families of Hard Planning Problems", AAAI 2014: 2337-2343 (2014), by E. G. Rie el, M. Do, D. Venturelli, I. Hen and J. Franks. [pdf]

"Fourier-transforming with quantum annealers". Front. Phys. 2, 44 (2014), by I. Hen [link]

"Optimized tomography for pure quantum states", [1409.1952], by A. Kalev and I. Hen.

 

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

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

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

 

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

"Detecting Entanglement with Partial State Information", Phys. Rev. A 86, 062311 (2012), by F. Spedalieri. [pdf]

"Adiabatic Quantum Algorithm for Search Engine Ranking", Phys. Rev. Lett. 108, 230506 (2012), by S. Garnerone, P. Zanardi, D. Lidar. [pdf]

“High-Fidelity Adiabatic Quantum Computation via Dynamical Decoupling”, Phys. Rev. A 86, 042333 (2012), by G. Quiroz and D. Lidar. [pdf]

"Excitation Gap from Optimized Correlation Functions in Quantum Monte Carlo Simulations", Phys. Rev. E 85, 036705 (2012) by I. Hen. [pdf]

"Solving the Graph Isomorphism Problem with a Quantum Annealer", Phys. Rev. A 86, 042310 (2012), I. Hen and A. P. Young. [pdf]

"The performance of the quantum adiabatic algorithm on 3 Regular 3XORSAT and 3 Regular Max-Cut", Phys. Rev. A 86, 052334 (2012), E. Farhi, D. Gosset, I. Hen, A. W. Sandvik, P. Shor, A. P. Young, and F. Zamponi. [pdf]