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M. Rosseinsky (2011)
Physical Review B
A. Penzkofer, W. Falkenstein (1976)
CHEMICAL PHYSICS LETTERS
Alex Selby (2014)
Efficient subgraph-based sampling of Ising-type models with frustrationarXiv: Statistical Mechanics
Demian Battaglia, G. Santoro, E. Tosatti (2005)
Optimization by quantum annealing: lessons from hard satisfiability problems.Physical review. E, Statistical, nonlinear, and soft matter physics, 71 6 Pt 2
Arman Zaribafiyan, D. Marchand, Seyed Rezaei (2016)
Systematic and deterministic graph minor embedding for Cartesian products of graphsQuantum Information Processing, 16
He Jiang, J. Xuan (2009)
Backbone Guided Local Search for the Weighted Maximum Satisfiability Problem
Andrew King (2015)
Performance of a quantum annealer on range-limited constraint satisfaction problemsArXiv, abs/1502.02098
This figure "1QBit_Short_CMYK.png" is available in "png"" format from: http://arxiv.org/ps/1606
H. Katzgraber, H. Katzgraber, F. Hamze, Zheng Zhu, Andrew Ochoa, Humberto Munoz-Bauza (2015)
Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the UglyarXiv: Quantum Physics
D. Daly, M. Eleftheriou, J. Moreira, K. Ryu (2009)
Proceedings of the 8th Workshop on High Performance Computational FinanceProceedings of the 8th Workshop on High Performance Computational Finance
T. Rønnow, Zhihui Wang, Joshua Job, S. Boixo, S. Isakov, D. Wecker, J. Martinis, Daniel Lidar, M. Troyer (2014)
Defining and detecting quantum speedupScience, 345
P. Chardaire, J. Lutton, Alain Sutter (1995)
Thermostatistical persistency: A powerful improving concept for simulated annealing algorithmsEuropean Journal of Operational Research, 86
(2014)
Applied Superconductivity IEEE Transactions on
S. Mandrà, Zheng Zhu, Wenlong Wang, A. Perdomo-Ortiz, H. Katzgraber (2016)
Strengths and weaknesses of weak-strong cluster problems: A detailed overview of state-of-the-art classical heuristics versus quantum approachesPhysical Review A, 94
Christopher Meek, M. Chickering, Joseph Halpern (2004)
Proceedings of the 20th conference on Uncertainty in artificial intelligence
B. Heim, T. Rønnow, S. Isakov, M. Troyer (2014)
Quantum versus classical annealing of Ising spin glassesScience, 348
F. Glover (1989)
Tabu Search - Part IINFORMS J. Comput., 1
G. Santoro, R. Martoňák, E. Tosatti, R. Car (2002)
Theory of Quantum Annealing of an Ising Spin GlassScience, 295
Catherine McGeoch, Cong Wang (2013)
Experimental evaluation of an adiabiatic quantum system for combinatorial optimization
A. Finnila, M. Gomez, C. Sebenik, C. Stenson, J. Doll (1994)
Quantum annealing: A new method for minimizing multidimensional functionsChemical Physics Letters, 219
(2013)
Journal of Heuristics
Martin Weigel, H. Katzgraber, Jonathan Machta (2014)
Glassy Chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machinesPhysical Review X, 4
P. Hammer, P. Hansen, B. Simeone (1984)
Roof duality, complementation and persistency in quadratic 0–1 optimizationMathematical Programming, 28
F. Glover (1989)
Tabu Search - Part IIINFORMS J. Comput., 2
P. Ray, B. Chakrabarti, A. Chakrabarti (1989)
Sherrington-Kirkpatrick model in a transverse field: Absence of replica symmetry breaking due to quantum fluctuations.Physical review. B, Condensed matter, 39 16
K. Pudenz, T. Albash, Daniel Lidar (2013)
Error-corrected quantum annealing with hundreds of qubitsNature Communications, 5
T. Kadowaki, H. Nishimori (1998)
Quantum annealing in the transverse Ising modelPhysical Review E, 58
M. Weigel, H. Katzgraber, J. Machta, F. Hamze, Ruben Andrist (2014)
Erratum: Glassy Chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machines (Phys. Rev. X 4, 021008 (2014))Physical Review X, 5
(2016)
D-Wave Systems. SAPI 2.3.1 documentation
A. Mishra, T. Albash, Daniel Lidar (2015)
Performance of two different quantum annealing correction codesQuantum Information Processing, 15
James King, S. Yarkoni, M. Nevisi, J. Hilton, Catherine McGeoch (2015)
Benchmarking a quantum annealing processor with the time-to-target metricarXiv: Quantum Physics
I. Zintchenko, M. Hastings, M. Troyer (2014)
From local to global ground states in Ising spin glassesPhysical Review B, 91
A. Perdomo-Ortiz, B. O’Gorman, J. Fluegemann, R. Biswas, V. Smelyanskiy (2015)
Determination and correction of persistent biases in quantum annealersScientific Reports, 6
W. Vinci, T. Albash, G. Paz-Silva, I. Hen, Daniel Lidar (2015)
Quantum annealing correction with minor embeddingPhysical Review A, 92
P. Hammer, E. Boros, Gabriel Tavares (2008)
New algorithms for quadratic unconstrained binary optimization (qubo) with applications in engineering and social sciences
G. Rosenberg, P. Haghnegahdar, Phil Goddard, P. Carr, Kesheng Wu, M. Prado (2015)
Solving the Optimal Trading Trajectory Problem Using a Quantum AnnealerIEEE Journal of Selected Topics in Signal Processing, 10
Mark Johnson, M. Amin, Gildert Suzanne, T. Lanting, F. Hamze, N. Dickson, R. Harris, A. Berkley, J. Johansson, P. Bunyk, E. Chapple, C. Enderud, J. Hilton, K. Karimi, E. Ladizinsky, N. Ladizinsky, T. Oh, I. Perminov, C. Rich, M. Thom, E. Tolkacheva, C. Truncik, S. Uchaikin, J. Wang, B. Wilson, G. Rose (2011)
Quantum annealing with manufactured spinsNature, 473
Yang Wang, Zhipeng Lü, F. Glover, Jin-Kao Hao (2011)
Effective Variable Fixing and Scoring Strategies for Binary Quadratic Programming
Bart Verheij, M. Wiering (2017)
Artificial Intelligence, 823
A. Perdomo-Ortiz, J. Fluegemann, R. Biswas, V. Smelyanskiy (2015)
A Performance Estimator for Quantum Annealers: Gauge selection and Parameter SettingarXiv: Quantum Physics
S. Boixo, T. Rønnow, S. Isakov, Zhihui Wang, D. Wecker, Daniel Lidar, J. Martinis, M. Troyer (2013)
Evidence for quantum annealing with more than one hundred qubitsNature Physics, 10
Weixiong Zhang (2004)
Configuration landscape analysis and backbone guided local search: Part I: Satisfiability and maximum satisfiabilityArtif. Intell., 158
A. Lucas (2013)
Ising formulations of many NP problemsArXiv, abs/1302.5843
(2016)
Proceedings of the ACM International Conference on Computing FrontiersProceedings of the ACM International Conference on Computing Frontiers
S. Boixo, V. Smelyanskiy, A. Shabani, S. Isakov, M. Dykman, Vasil Denchev, M. Amin, A. Smirnov, M. Mohseni, H. Neven (2016)
Computational multiqubit tunnelling in programmable quantum annealersNature Communications, 7
W. Lechner, P. Hauke, P. Zoller (2015)
A quantum annealing architecture with all-to-all connectivity from local interactionsScience Advances, 1
(2013)
QUBO-Chimera. " https://github.com/alex1770/QUBO- Chimera
I. Hen, Joshua Job, T. Albash, T. Rønnow, M. Troyer, Daniel Lidar (2015)
Probing for quantum speedup in spin-glass problems with planted solutionsPhysical Review A, 92
R. Brightman (1948)
Science AdvancesNature, 161
M. Middendorf, C. Blum (2015)
Evolutionary Computation in Combinatorial Optimization, 9026
(1989)
ORSA Journal on computing
T. Lanting, A. Przybysz, A. Smirnov, F. Spedalieri, M. Amin, A. Berkley, R. Harris, F. Altomare, S. Boixo, P. Bunyk, N. Dickson, C. Enderud, J. Hilton, E. Hoskinson, M. Johnson, E. Ladizinsky, N. Ladizinsky, R. Neufeld, T. Oh, I. Perminov, C. Rich, M. Thom, E. Tolkacheva, S. Uchaikin, A. Wilson, G. Rose (2014)
Entanglement in a Quantum Annealing ProcessorPhysical Review X, 4
K. Pudenz, T. Albash, Daniel Lidar (2014)
Quantum annealing correction for random Ising problemsPhysical Review A, 91
(2010)
Nature CommunicationsNature Cell Biology, 12
(1995)
Physical Review E
F. Hamze, Nando Freitas (2004)
From Fields to Trees
K. Ratnavelu (2009)
FRONTIERS IN PHYSICS
(1996)
Physical Review A
R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman, Lidror Troyansky (1999)
Determining computational complexity from characteristic ‘phase transitions’Nature, 400
Yang Wang, Zhipeng Lü, F. Glover, Jin-Kao Hao (2013)
Backbone guided tabu search for solving the UBQP problemJournal of Heuristics, 19
Vasil Denchev, S. Boixo, S. Isakov, Nan Ding, R. Babbush, V. Smelyanskiy, J. Martinis, H. Neven (2015)
What is the Computational Value of Finite Range TunnelingarXiv: Quantum Physics
Siddharth Muthukrishnan, T. Albash, Daniel Lidar (2015)
Tunneling and speedup in quantum optimization for permutation-symmetric problemsarXiv: Quantum Physics
P. Bunyk, E. Hoskinson, Mark Johnson, E. Tolkacheva, F. Altomare, A. Berkley, R. Harris, J. Hilton, T. Lanting, A. Przybysz, J. Whittaker (2014)
Architectural Considerations in the Design of a Superconducting Quantum Annealing ProcessorIEEE Transactions on Applied Superconductivity, 24
Tony Tran, M. Do, E. Rieffel, J. Frank, Zhihui Wang, B. O’Gorman, D. Venturelli, J. Beck (2016)
A Hybrid Quantum-Classical Approach to Solving Scheduling Problems
R. Słowiński, J. Oliveira, S. Rebennack, R. Teunter, M. Yearworth, A. Mercer, B. Tilanus, H.-J. Zimmermann, C. Archetti, R. Bai, A. Barbosa‐Póvoa, Técnico Lisboa, A. Ben-Tal, J. Billaut, N. Boysen, S. Bozoki, J. Branke, D. Briskorn, M. Caramia, J. Carlier, J. Crook, F. Croce, E. Demeulemeester, KU Leuven, Belgium Leuven, Disney, M. Doumpos, M. Figueira, L. Franco, A. Gavious, J. Gondzio, S. Greco, V. Hemmelmayr, S. Howick, M. Inuiguchi, F. Jaehn, M. Johnson, D. Kadziński, C. Kao, G. Kou, M. Kovalyov, T. Liu, R. M’Hallah, R. Martí, J. Martinez, B. Matarazzo, W. Michalowski, A. Minca, A. Nagurney, Y. Peng, U. Pferschy, M. Recchioni, R. Ruiz, S. Salman, C. Schwindt, D. Shabtay, S. Shen, T. Spengler, F. Toledo, P. Toth, D. Tuyttens, G. Berghe, R. Vetschera, J. Wallenius, J. Weglarz, D. Wozabal, H. Xu, X. Zhou, J. Zhu (2008)
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
V. Choi (2010)
Minor-embedding in adiabatic quantum computation: II. Minor-universal graph designQuantum Information Processing, 10
G. Rosenberg, Mohammadreza Vazifeh, Brad Woods, E. Haber (2015)
Building an iterative heuristic solver for a quantum annealerComputational Optimization and Applications, 65
E. Boros, P. Hammer, Gabriel Tavares (2006)
Preprocessing of unconstrained quadratic binary optimization
F. Pastawski, J. Preskill (2015)
Error correction for encoded quantum annealingPhysical Review A, 93
(2014)
Przybysz, et al., Applied Superconductivity
V. Choi (2008)
Minor-embedding in adiabatic quantum computation: I. The parameter setting problemQuantum Information Processing, 7
Federico Romá, S. Risau-Gusman (2013)
Backbone structure of the Edwards-Anderson spin-glass model.Physical review. E, Statistical, nonlinear, and soft matter physics, 88 4
We propose a novel method for reducing the number of variables in quadratic unconstrained binary optimization problems, using a quantum annealer (or any sampler) to fix the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are usually much easier for the quantum annealer to solve, due to their being smaller and consisting of disconnected components. This approach significantly increases the success rate and number of observations of the best known energy value in samples obtained from the quantum annealer, when compared with calling the quantum annealer without using it, even when using fewer annealing cycles. Use of the method results in a considerable improvement in success metrics even for problems with high-precision couplers and biases, which are more challenging for the quantum annealer to solve. The results are further enhanced by applying the method iteratively and combining it with classical pre-processing. We present results for both Chimera graph-structured problems and embedded problems from a real-world application.
Quantum Information Processing – Springer Journals
Published: May 16, 2017
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