Bibliography#

[Ada2015]

Steven H. Adachi, Maxwell P. Henderson. “Application of Quantum Annealing to Training of Deep Neural Networks.” arXiv:1510.06356. 21 Oct 2015. https://arxiv.org/abs/1510.06356

[Ahu2000]

Ahuja, R. K., Orlin, J. B., and Sharma, D. “Very large-scale neighborhood search.” International Transactions in Operational Research 7 (4-5): 301-317. 2000. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1475-3995.2000.tb00201.x

[Arr2022]

Federica Arrigoni, Willi Menapace, Marcel Seelbach Benkner, Elisa Ricci, and Vladislav Golyanik. “Quantum Motion Segmentation.” arXiv:2203.13185 [cs.CV] https://doi.org/10.48550/arXiv.2203.13185

[Bac2002]

Fahiem Bacchus, Xinguang Chen, Peter van Beek, and Toby Walsh. “Binary vs. non-binary constraints.” Artificial Intelligence 140 (2002) 1–37. 17 January 2001. https://www.sciencedirect.com/science/article/pii/S0004370202002102

[Bar1982]

F. Barahona. “On the Computational Complexity of Ising Spin Glass Models.” J. Phys. A, 15, 3241-3253, 1982.

[Bas2020]

Gideon Bass, Max Henderson, Joshua Heath, Joseph Dulny III. “Optimizing the Optimizer: Decomposition Techniques for Quantum Annealing.” Quantum Mach. Intell. 3, 10 (2021). https://doi.org/10.1007/s42484-021-00039-9 https://arxiv.org/abs/2001.06079

[Ben2017]

Marcello Benedetti, John Realpe-G´omez, Rupak Biswas,and Alejandro Perdomo-Ortiz. “Quantum-assisted learning of hardware-embedded probabilistic graphical models.” arXiv:1609.02542v3 19 Oct 2017 https://arxiv.org/abs/1609.02542

[Ber1999]

D. Bertsimas and C.-P. Teo and R. Vohra. “On dependent randomized rounding algorithms.” Oper. Res. Lett., 24(3), 105-114, May 1996. http://www.mit.edu/~dbertsim/papers/ApproximationAlgorithms/On%20dependent%20randomized%20rounding%20algorithms.pdf

[Bia2010]

Zhengbing Bian, Fabian Chudak, William G. Macready, and Geordie Rose. “The Ising model: teaching an old problem new tricks.” August 30, 2010. The D-Wave website.

[Bia2014]

Zhengbing Bian, Fabian Chudak, Robert Israel, Brad Lackey, William G. Macready, and Aidan Roy. “Discrete optimization using quantum annealing on sparse Ising models.” Frontiers in Physics 18 September 2014. https://www.frontiersin.org/articles/10.3389/fphy.2014.00056/full

[Bia2016]

Zhengbing Bian, Fabian Chudak, Robert Israel, Brad Lackey, William G. Macready, and Aidan Roy. “Mapping Constrained Optimization Problems to Quantum Annealing with Application to Fault Diagnosis.” Frontiers in Physics 28 July 2016. https://www.frontiersin.org/articles/10.3389/fict.2016.00014/full

[Bia2017]

Zhengbing Bian, Fabian Chudak, William Macready, Aidan Roy, Roberto Sebastiani, and Stefano Varotti. “Solving SAT and MaxSAT with a Quantum Annealer: Foundations and a Preliminary Report.” Frontiers of Combining Systems pp 153-171. Lecture Notes in Computer Science vol 10483. Springer, Cham. 29 August 2017. https://link.springer.com/chapter/10.1007/978-3-319-66167-4_9

[Bir2021]

Tolga Birdal, Vladislav Golyanik, Christian Theobalt, and Leonidas Guibas. “Quantum Permutation Synchronization.” arXiv:2101.07755 [quant-ph]

[Bis2017]

Rupak Biswas, Zhang Jiang, Kostya Kechezhi, Sergey Knysh, Salvatore Mandr`a, Bryan O’Gorman, Alejandro Perdomo-Ortiz, Andre Petukhov, John Realpe-G´omez, Eleanor Rieffel, Davide Venturelli, Fedir Vasko, Zhihui Wang. “A NASA Perspective on Quantum Computing: Opportunities and Challenges.” arXiv:1704.04836v1 17 Apr 2017 https://arxiv.org/abs/1704.04836

[Bod1994]

Bodlaender, H.L., Jansen, K. “On the complexity of the maximum cut problem.” Enjalbert, P., Mayr, E.W., Wagner, K.W. (eds) STACS 94. STACS 1994. Lecture Notes in Computer Science, vol 775. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57785-8_189

[Bon2007]

María Luisa Bonet, Jordi Levy, and Felip Manyà. “Resolution for Max-SAT” Artificial Intelligence, Volume 171, Issues 8–9, Pages 606-618. June 2007. https://www.sciencedirect.com/science/article/pii/S0004370207000422

[Boo2016]

Kelly Boothby, Andrew D. King, and Aidan Roy. “Fast clique minor generation in Chimera qubit connectivity graphs.” Quantum Inf Process Volume 15, Issue 1, pp 495–508. January 2016. https://link.springer.com/article/10.1007/s11128-015-1150-6 https://arxiv.org/abs/1507.04774

[Bor2002]

E. Boros and P. L. Hammer. “Pseudo-boolean optimization.” Discrete Appl. Math., 123, 155-225 2002. https://www.sciencedirect.com/science/article/pii/S0166218X01003419.

[Boy2007]

S. Boyd and A. Mutapcic. “Subgradient methods.” 2007. https://web.stanford.edu/class/ee364b/lectures/subgrad_method_notes.pdf

[Bru1967]

Stephen G. Brush. “History of the Lenz-Ising Model.” Rev. Mod. Phys. 39, 883. October 1967. https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.39.883

[Bru1994]

Peter Brucker, Bernd Jurisch, and Bernd Sievers. “A branch and bound algorithm for the job-shop scheduling problem.” Discrete Applied Mathematics Volume 49, Issues 1–3, 30 March 1994. https://www.sciencedirect.com/science/article/pii/0166218X94902046

[Bun2014]

P. I. Bunyk et al. “Architectural considerations in the design of a superconducting quantum annealing processor.” IEEE Transactions on Applied Superconductivity 24.4 (Aug. 2014), pp. 1–10. https://arxiv.org/abs/1401.5504

[Bur2002]

C.J.C. Burges. “Factoring as Optimization.” Microsoft Research TR-2002-83. 2002. https://www.microsoft.com/en-us/research/publication/factoring-as-optimization/

[Cai2014]

Jun Cai, William G. Macready, and Aidan Roy. “A practical heuristic for finding graph minors.” arXiv. 10 Jun 2014. https://arxiv.org/abs/1406.2741

[Cha2019]

Nicholas Chancellor. “Domain wall encoding of discrete variables for quantum annealing and QAOA.” 12 Mar 2019. Quantum Science and Technology 4, 045004 (2019). https://arxiv.org/abs/1903.05068

[Che2021]

Jie Chen, Tobias Stollenwerk, and Nicholas Chancellor. “Performance of Domain-Wall Encoding for Quantum Annealing”. 24 February 2021. https://arxiv.org/abs/2102.12224v1

[Coh2020]

Jeffrey Cohen, Alex Khan, Clark Alexander “Portfolio Optimization of 60 Stocks Using Classical and Quantum Algorithms.” arXiv:2008.08669v1 [q-fin.GN]

[Coh2020b]

Jeffrey Cohen, Clark Alexander “Picking Efficient Portfolios from 3,171 US Common Stocks with New Quantum and Classical Solvers.” arXiv:2011.01308v1 [quant-ph]

[Coo1971]

S. Cook. “The Complexity of Theorem-Proving Procedures.” Proceedings of 3rd annual ACM Symposium on Theory of Computing, 151-158, 1971. https://dl.acm.org/doi/10.1145/800157.805047

[Cou2009]

James Coughlan. “A tutorial introduction to belief propagation.” 2009. https://www.ski.org/sites/default/files/publications/bptutorial.pdf

[Das2019]

Samudra Dasgupta, Arnab Banerjee. “Quantum Annealing Algorithm for Expected Shortfall based Dynamic Asset Allocation.” arXiv:1909.12904v4 [q-fin.RM]

[Dat2014]

Nikesh S. Dattani and Nathaniel Bryans. “Quantum factorization of 56153 with only 4 qubits.” 25 Nov 2014 https://arxiv.org/abs/1411.6758

[Dec1987]

R. Dechter and J. Pearl. “The cycle-cutset method for improving search performance in AI applications.” Proceedings of the Third IEEE on Artificial Intelligence Applications, pp. 224–230. 1987.

[Dec2003]

Rina Dechter. “Constraint Processing.” Morgan Kaufmann. 2003.

[Dic2013]

N. G. Dickson et al. “Thermally assisted quantum annealing of a 16-qubit problem.” Nature communications. 4:1903. May 21, 2013. https://www.nature.com/articles/ncomms2920

[Din2019]

Yongcheng Ding, Xi Chen, Lucas Lamata, Enrique Solano, Mikel Sanz “Implementation of a Hybrid Classical-Quantum Annealing Algorithm for Logistic Network Design.” arXiv:1906.10074 [quant-ph]

[Dou2015]

Adam Douglass, Andrew D. King, and Jack Raymond. “Constructing SAT Filters with a Quantum Annealer.” In: Heule M., Weaver S. (eds) Theory and Applications of Satisfiability Testing – SAT 2015. SAT 2015. Lecture Notes in Computer Science, vol 9340. Springer, Cham

[Dum2013]

Vincent Dumoulin, Ian J. Goodfellow, Aaron Courville, and Yoshua Bengio. “On the Challenges of Physical Implementations of RBMs.” arXiv:1312.5258 18 Dec 2013. https://arxiv.org/abs/1312.5258

[Dri2017]

Raouf Dridi and Hedayat Alghassi. “Prime factorization using quantum annealing and computational algebraic geometry.” https://www.nature.com/articles/srep43048.pdf

[Dwave3]

Evgeny Andriyash, Zhengbing Bian, Fabian Chudak, Marshall Drew-Brook, Andrew D. King, William G. Macready, and Aidan Roy. “Boosting integer factoring performance via quantum annealing offsets.” https://www.dwavesys.com/media/l0tjzis2/14-1002a_b_tr_boosting_integer_factorization_via_quantum_annealing_offsets.pdf

[Dwave4]

“Programming with D-Wave: Map Coloring Problem”. D-Wave website under Resources/Publications. 2013. https://www.dwavesys.com/media/htfgw5bk/map-coloring-wp2.pdf

[Dwave5]

“Reverse Annealing for Local Refinement of Solutions.” D-Wave White Paper Series, no. 14-1018A-A. 2017. https://www.dwavesys.com/resources/publications.

[Dwave6]

“Virtual Graphs for High-Performance Embedded Topologies.” D-Wave White Paper Series, no. 14-1020A, 2017. https://www.dwavesys.com/resources/publications.

[Dwave7]

“D-Wave Hybrid Solver Service + Advantage: Technology Update” D-Wave Technical Report, no. 14-1048A-A, 2020. https://www.dwavesys.com/media/m2xbmlhs/14-1048a-a_d-wave_hybrid_solver_service_plus_advantage_technology_update.pdf

[Efr1982]

B. Efron, (1982). “The Jackknife, the Bootstrap, and Other Resampling Plans.”” Philadelphia, PA: Society for Industrial and Applied Mathematics. ISBN 9781611970319.

[Els2017]

Nada Elsokkary, Faisal Shah Khan, Davide La Torre, Travis S. Humble, and Joel Gottlieb. “Financial Portfolio Management using D-Wave’s Quantum Optimizer: The Case of Abu Dhabi Securities Exchange.”

[Flo2017]

Florian Neukart, Gabriele Compostella, Christian Seidel, David von Dollen, Sheir Yarkoni, and Bob Parney. “Traffic flow optimization using a quantum annealer.” arXiv:1708.01625. 4 Aug 2017. https://arxiv.org/abs/1708.01625

[Fra1989]

Francesca Rossi, Charles Petrie, and Vasant Dhar. “On the Equivalence of Constraint Satisfaction Problems.” Technical Report ACTAI-222-89, MCC, Austin, TX. 1989.

[Glo1990]

Glover, F. “Tabu Search: A Tutorial.” Interfaces July/August 1990 20:74-94. https://www.ida.liu.se/~zebpe83/heuristic/papers/TS_tutorial.pdf

[Glo2017]

Fred Glover, Mark Lewis, and Gary Kochenberger. “Logical and Inequality Implications for Reducing the Size and Complexity of Quadratic Unconstrained Binary Optimization Problems.” arXiv:1705.09545. https://arxiv.org/abs/1705.09545

[Gol2019]

Vladislav Golyanik and Christian Theobalt. “A Quantum Computational Approach to Correspondence Problems on Point Sets.” arXiv:1912.12296 [cs.CV]

[Gra2021]

Erica Grant, Travis S. Humble, and Benjamin Stump “Benchmarking Quantum Annealing Controls with Portfolio Optimization” Phys. Rev. Applied 15, 014012 – Published 8 January 2021 https://arxiv.org/pdf/2007.03005.pdf

[Gra2008]

P. Grassberger, (2008). “Entropy Estimates from Insufficient Samplings.” arXiv:physics/0307138v2.

[Gue2018]

G. G. Guerreschi, A. Y. Matsuura. “QAOA for Max-Cut requires hundreds of qubits for quantum speed-up.” https://doi.org/10.48550/arXiv.1812.07589. https://arxiv.org/abs/1812.07589

[Har2009]

R. Harris et al. “Compound Josephson-junction coupler for flux qubits with minimal crosstalk.” Phys. Rev. B 80, (20 Aug. 2009). https://journals.aps.org/prb/abstract/10.1103/PhysRevB.80.052506.

[Har2010]

R. Harris et al. “Experimental demonstration of a robust and scalable flux qubit.” Phys. Rev. B 81. 13 Apr. 2010. https://arxiv.org/abs/0909.4321

[Har2010_2]

R. Harris et al. “Experimental investigation of an eight qubit unit cell in a superconducting optimization processor.” Phys. Rev. B 82, 024511 (2010) arXiv:1004.1628

[Hig2022]

Catherine F. Higham, Desmond J. Higham, and Francesco Tudisco. “Testing a QUBO Formulation of Core-periphery Partitioning on a Quantum Annealer.” arXiv:2201.01543 [cs.SI]

[Hin2012]

Geoffrey E. Hinton. “A Practical Guide to Training Restricted Boltzmann Machines.” Pages 599–619, Springer, Berlin, Heidelberg . 2012. https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf

[Ike2019]

Ikeda, K., Nakamura, Y. & Humble, T.S. “Application of Quantum Annealing to Nurse Scheduling Problem.” Sci Rep 9, 12837 (2019). https://doi.org/10.1038/s41598-019-49172-3

[Inc2022]

Massimiliano Incudini, Fabio Tarocco, Riccardo Mengoni, Alessandra Di Pierro, and Antonio Mandarino. “Computing Graph Edit Distance with Algorithms on Quantum Devices.” arXiv:2111.10183 [quant-ph]

[Ish2011]

Hiroshi Ishikawa. “Transformation of General Binary MRF Minimization to the First-Order Case.” IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 33, NO. 6. June 2011. https://ieeexplore.ieee.org/document/5444874/

[Izq2022]

Zoe Gonzalez Izquierdo, Shon Grabbe, Husni Idris, Zhihui Wang, Jeffrey Marshall, and Eleanor Rieffel. “The Advantage of pausing: parameter setting for quantum annealers.” arXiv:2205.12936 [quant-ph]

[Jas2019]

Tim Jaschek, Marko Bucyk, and Jaspreet S. Oberoi. “A Quantum Annealing-Based Approach to Extreme Clustering.” https://1qbit.com/our-thinking/research-papers arXiv:1903.08256 [cs.LG]

[Jen1990]

F.V. Jensen, S.L. Lauritzen, and K.G. Olesen (1990). “Bayesian updating in causal probabilistic networks by local computations.” Computational Statistics Quarterly, vol. 4, p. 269-282.

[Jia2018]

Shuxian Jiang, Keith A. Britt, Alexander J. McCaskey, Travis S. Humble, and Sabre Kais. “Quantum Annealing for Prime Factorization.” https://www.nature.com/articles/s41598-018-36058-z.pdf.

[Joh2007]

J. K. Johnson, D. M. Malioutov, and A. S. Willsky. “Lagrangian relaxation for MAP estimation in graphical models.” Proceedings of The 45th Allerton Conference on Communication, Control and Computing. Sept. 2007.

[Joh2010]

M. W. Johnson et al. “A scalable control system for a superconducting adiabatic quantum optimization processor.” Superconductor Science and Technology 23.6 (2010). https://iopscience.iop.org/article/10.1088/0953-2048/23/6/065004

[Joh2011]

M. W. Johnson et al. “Quantum annealing with manufactured spins.” Nature 473 (May 12, 2011), pp. 194–198.

[Jor2011]

Stephen Jordan. “Quantum Algorithm Zoo.” https://math.nist.gov/quantum/zoo/

[Jue2016]

Juexiao Su, Tianheng Tu, and Lei He. “A quantum annealing approach for Boolean Satisfiability problem.” Design Automation Conference (DAC), 2016 53nd ACM/EDAC/IEEE. https://ieeexplore.ieee.org/document/7544390/

[Kal2019]

Angad Kalra, Faisal Qureshi, Michael Tisi. “Portfolio Asset Identification Using Graph Algorithms on a Quantum Annealer.” Available at SSRN: https://ssrn.com/abstract=3333537 or http://dx.doi.org/10.2139/ssrn.3333537

[Kar2017]

Sahar Karimi and Pooya Ronagh. “Practical Integer-to-Binary Mapping for Quantum Annealers.” arXiv:1706.01945 [quant-ph] https://doi.org/10.48550/arXiv.1706.01945

[Kin2014]

A. D. King and C. C. McGeoch. “Algorithm engineering for a quantum annealing platform.” arXiv preprint arXiv:1410.2628 (2014). https://arxiv.org/abs/1410.2628

[Kin2016]

A. D. King et al. “Degeneracy, degree, and heavy tails in quantum annealing.” Physical Review A 93.5 (2016): 052320. https://arxiv.org/abs/1512.07325

[Kin2021]

King, A.D., Raymond, J., Lanting, T. et al. “Scaling advantage over path-integral Monte Carlo in quantum simulation of geometrically frustrated magnets.” Nat Commun 12, 1113 (2021). https://www.nature.com/articles/s41467-021-20901-5

[Koc2004]

G. Kochenberger et al. “A unified modeling and solution framework for combinatorial optimization problems.” OR Spectrum 26 (2004), pp. 237–250. http://leeds-faculty.colorado.edu/glover/fred%20pubs/333%20-%20xQx%20-%20Unified%20modeling%20and%20solution%20framework%20(short).doc.

[Koh2022]

Yang Wei Koh, Hidetoshi Nishimori “Quantum and classical annealing in a continuous space with multiple local minima.” arXiv:2203.11417 [quant-ph] https://doi.org/10.48550/arXiv.2203.11417

[Kol2004]

V. Kolmogorov and R. Zabih. “What energy functions can be minimized via graph cuts?” IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 65-81. 2004. http://www.cs.cornell.edu/rdz/Papers/KZ-ECCV02-graphcuts.pdf.

[Kor2016]

Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian, Fabian Chudak, William G. Macready, Jason Rolfe, and Evgeny Andriyash. “Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines.” arXiv:1611.04528. 14 Nov 2016. https://arxiv.org/abs/1611.04528

[Kur2020]

Kurowski K., Weglarz J., Subocz M., Rozycki R., Waligora G. (2020) “Hybrid Quantum Annealing Heuristic Method for Solving Job Shop Scheduling “Problem. Krzhizhanovskaya V. et al. (eds) Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12142. Springer, Cham. https://link.springer.com/chapter/10.1007%2F978-3-030-50433-5_39

[Lan2017]

Trevor Lanting, Andrew D. King, Bram Evert, Emile Hoskinson. “Experimental demonstration of perturbative anticrossing mitigation using non-uniform driver Hamiltonians.” Phys. Rev. A 96, 042322 (2017) https://arxiv.org/abs/1708.03049

[Lec2006]

Yann LeCun. “Predicting structured outputs.” A Tutorial on Energy-Based Learning, MIT Press. 2006. http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf

[Lev1973]

Leonid Levin. “Universal search problems.” 1973. Translated into English by B. A. Trakhtenbrot “A survey of Russian approaches to perebor (brute-force searches) algorithms.” Annals of the History of Computing 6(4),384-400. 1984.

[Li2020]

Junde Li and Swaroop Ghosh. “Quantum-soft QUBO Suppression for Accurate Object Detection.” arXiv:2007.13992 [cs.CV]

[Lin2021]

Jian Lin, Zhengfeng Zhang, Junping Zhang, and Xiaopeng Li. “Hard instance learning for quantum adiabatic prime factorization.” arXiv:2110.04782 [quant-ph]

[Liu2005]

W. Liu et al. “A hybrid multi-exchange local search for unconstrained binary quadratic program.” University of Mississippi, Hearin Center for Enterprise Science,HCES-09-05. 2005.

[Liu2020]

C. -L. Liu, C. -C. Chang and C. -J. Tseng. “Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems.” IEEE Access, vol. 8, pp. 71752-71762, 2020, doi: 10.1109/ACCESS.2020.2987820. https://ieeexplore.ieee.org/abstract/document/9066984

[Lod2020]

Bas Lodewijks. “Mapping NP-hard and NP-complete optimisation problems to Quadratic Unconstrained Binary Optimisation problems.” https://doi.org/10.48550/arXiv.1911.08043. https://arxiv.org/abs/1911.08043

[Luc2013]

Andrew Lucas. “Ising formulations of many NP problems.” arXiv:1302.5843. 23 Feb 2013. https://arxiv.org/abs/1302.5843

[Mac2018]

Maciej Koch-Janusz and Zohar Ringel. “Mutual Information, Neural Networks and the Renormalization Group.” 24 Sep 2018. https://arxiv.org/pdf/1704.06279.pdf

[Mar1957]

H.M. Markowitz (1957). “The Elimination Form of the Inverse and its Application to Linear Programming” Management Science, vol. 3, no. 3, p. 255-269.

[Mar2007]

R. Marinescu and R. Dechter. “Best-first AND/OR search for 0-1 integer linear programming.” Proceedings of the 4th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR). 2007. https://www.ics.uci.edu/~csp/r140.pdf.

[Mar2018]

D. J. J. Marchand, M. Noori, A. Roberts, G. Rosenberg, B. Woods, U. Yildiz, M. Coons, D. Devore, and P. Margl. “A Variable Neighbourhood Descent Heuristic for Conformational Search Using a Quantum Annealer.” https://1qbit.com/our-thinking/research-papers arXiv:1811.06999 [quant-ph]

[Mni2021]

Susan M. Mniszewski, Pavel A. Dub, Sergei Tretiak, Petr M. Anisimov, Yu Zhang and Christian F. A. Negre. “Reduction of the molecular hamiltonian matrix using quantum community detection.” Sci Rep 11, 4099 (2021). https://doi.org/10.1038/s41598-021-83561-x https://www.ics.uci.edu/~csp/r140.pdf. https://www.nature.com/articles/s41598-021-83561-x

[Muc2022]

Sascha Mücke, Raoul Heese, Sabine Müller, Moritz Wolter, and Nico Piatkowski. “Quantum Feature Selection.” arXiv:2203.13261 [quant-ph] https://doi.org/10.48550/arXiv.2203.13261

[Mug2020]

Samuel Mugel, Carlos Kuchkovsky, Escolastico Sanchez, Samuel Fernandez-Lorenzo, Jorge Luis-Hita, Enrique Lizaso, and Roman Orus. “Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks” arXiv:2007.00017

[Mug2021]

Samuel Mugel, Mario Abad, Miguel Bermejo, Javier Sanchez, Enrique Lizaso & Roman Orus “Hybrid quantum investment optimization with minimal holding period.” Nature Scientific Reports, 2021 https://www.nature.com/articles/s41598-021-98297-x

[Nar2017]

Ali Narimani, Seyed Saeed Changiz Rezaei, and Arman Zaribafiyan. “Combinatorial Optimization by Decomposition on Hybrid CPU–non-CPU Solver Architectures.” https://1qbit.com/our-thinking/research-papers arXiv:1708.03439

[Nev2012]

Neven, H., Denchev, V. S., Rose, G., and Macready, W. G. “QBoost: Large Scale Classifier Training with Adiabatic Quantum Optimization.” Journal of Machine Learning Research: Workshop and Conference Proceedings, 2012. http://proceedings.mlr.press/v25/neven12/neven12.pdf

[Ngu2019]

Nga T.T. Nguyen, Garrett T. Kenyon, and Boram Yoon. “A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer.” Sci Rep 10, 10915 (2020). arXiv:1911.06267v2 [quant-ph]

[Ohz2020]

Masayuki Ohzeki. “Breaking limitation of quantum annealer in solving optimization problems under constraints.” arXiv:2002.05298 [quant-ph]

[Oru2019]

Orus, Roman & Mugel, Samuel & Lizaso, Enrique. (2019). “Forecasting financial crashes with quantum computing.” Physical Review A. 99. 10.1103/PhysRevA.99.060301

[Pal2021]

Samuel Palmer, Serkan Sahin, Rodrigo Hernandez, Samuel Mugel, Roman Orus “Quantum Portfolio Optimization with Investment Bands and Target Volatility.” arXiv:2106.06735v4 [q-fin.PM]

[Pap1976]

Spiros Papaioannou. “Optimal Test Generation in Combinational Networks by Pseudo-Boolean Programming.” IEEE Transactions on Computers > Volume: C-26 Issue: 6. 24 May 1976. https://ieeexplore.ieee.org/document/1674880/?arnumber=1674880

[Pea2008]

J. Pearl. “Probabilistic Reasoning in Intelligent Systems.” 2nd ed. San Francisco, CA: Kaufmann, 1988.

[Pel2021]

Pelofske, E., Hahn, G. and Djidjev, H.N. “Parallel quantum annealing.” Sci Rep 12, 4499 (2022). https://doi.org/10.1038/s41598-022-08394-8

[Per2012]

Alejandro Perdomo-Ortiz, Neil Dickson, Marshall Drew-Brook, Geordie Rose, and Alán Aspuru-Guzik. “Finding low-energy conformations of lattice protein models by quantum annealing.” Scientific Reports 2, Article number: 571. 2012. https://www.nature.com/articles/srep00571

[Per2015]

A. Perdomo-Ortiz, J. Fluegemann, S. Narasimhan, R. Biswas, and V. N. Smelyanskiy “A quantum annealing approach for fault detection and diagnosis of graph-based systems.” European Physical Journal Special Topics, vol. 224, Feb. 2015. https://arxiv.org/abs/1406.7601v2

[Per2022]

David Peral García, Juan Cruz-Benito, and Francisco José García-Peñalvo. “Systematic Literature Review: Quantum Machine Learning and its applications.” arXiv:2201.04093 [quant-ph]

[Phi2021]

Phillipson F., Bhatia H.S. “Portfolio Optimisation Using the D-Wave Quantum Annealer.” In: Paszynski M., Kranzlmüller D., Krzhizhanovskaya V.V., Dongarra J.J., Sloot P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_4

[Pud2014]

K. L. Pudenz et al. “Error-corrected quantum annealing with hundreds of qubits.” Nature communications 5 (2014). https://www.nature.com/articles/ncomms4243

[Pud2015]

K. L. Pudenz et al. “Quantum annealing correction for random Ising problems.” Physical Review A 91.4 (2015): 042302. https://journals.aps.org/pra/abstract/10.1103/PhysRevA.91.042302

[Qui2021]

Rodolfo Quintero, David Bernal, Tamas Terlaky, and Luis F. Zuluaga. “Characterization of QUBO reformulations for the maximum k-colorable subgraph problem.” arXiv:2101.09462 [quant-ph]

[Ray2016]

J. Raymond, S. Yarkoni and E. Andriyash (2016). “Global Warming: Temperature Estimation in Annealers” arXiv:1606.00919.

[Ret2017]

Jacob Retallick, Michael Babcock, Miguel Aroca-Ouellette, Shane McNamara, Steve Wilton, Aidan, Mark Johnson, and Konrad Walus. “Algorithms for Embedding Quantum-Dot Cellular Automata Networks onto a Quantum Annealing Processor.” 14 September 2017. arXiv:1709.04972 https://arxiv.org/abs/1709.04972

[Rie2014]

Eleanor G. Rieffel, Davide Venturelli, Bryan O’Gorman, Minh B. Do, Elicia Prystay, and Vadim N. Smelyanskiy. “A case study in programming a quantum annealer for hard operational planning problems.” arXiv:1407.2887 [quant-ph] 10 Jul 2014 https://arxiv.org/abs/1407.2887v1

[Rol2016]

Jason Tyler Rolfe. “Discrete Variational Autoencoders.” arXiv:1609.02200. 7 Sep 2016. https://arxiv.org/abs/1609.02200

[Ros2016]

G. Rosenberg et al. “Building an iterative heuristic solver for a quantum annealer.” Computational Optimization and Applications. Springer (2016), pp. 1-25. https://arxiv.org/abs/1507.07605

[Ros2016a]

Gili Rosenberg, Poya Haghnegahdar, Phil Goddard, Peter Carr, Kesheng Wu, and Marcos López de Prado. “Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer.” arXiv:1508.06182v3. 11 Aug 2016. https://arxiv.org/pdf/1508.06182.pdf

[Sal2007]

Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. “Restricted Boltzmann Machines for Collaborative Filtering.” The International Machine Learning Society. 2007.

[Sch2009]

Nicol N. Schraudolph and Dmitry Kamenetsky. “Efficient exact inference in planar Ising models.” Advances in Neural Information Processing Systems 21, MIT Press. 2009. http://users.cecs.anu.edu.au/~dkamen/nips08.pdf.

[Tam2022]

Toufan D. Tambunan, Andriyan B. Suksmono, Ian J.M. Edward, and Rahmat Mulyawan. “Quantum Annealing for Vehicle Routing Problem with weighted Segment.” arXiv:2203.13469 [quant-ph]. https://doi.org/10.48550/arXiv.2203.13469

[Tan2015]

Richard Tanburn, Emile Okada, and Nike Dattani. “Reducing multi-qubit interactions in adiabatic quantum computation without adding auxiliary qubits. Part 1: The “deduc-reduc” method and its application to quantum factorization of numbers.” arXiv:1508.04816 19 Aug 2015 https://arxiv.org/abs/1508.04816

[Tep2021]

Alexander Teplukhin, Brian K. Kendrick, Susan M. Mniszewski, Yu Zhang, Ashutosh Kumar, Christian F.A. Negre, Petr M. Anisimov, Sergei Tretiak and Pavel A. Dub. “Computing molecular excited states on a D‑Wave quantum annealer.” https://www.nature.com/articles/s41598-021-98331-y.pdf arXiv:2107.00162 [physics.chem-ph]

[Tin2018]

Ting-Jui Hsu, Fengping Jin, Christian Seidel, Florian Neukart, Hans De Raedt, Kristel Michielsen. “Quantum annealing with anneal path control: application to 2-SAT problems with known energy landscapes.” 2018. https://arxiv.org/abs/1810.00194v2

[Ush2017]

Hayato Ushijima-Mwesigwa, Christian F. A. Negre, and Susan M. Mniszewski. “Graph Partitioning using Quantum Annealing on the D-Wave System.” arXiv:1705.03082. 4 May 2017. https://arxiv.org/abs/1705.03082v1.

[Vah2017]

Arash Vahdat. “Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks.” arXiv:1706.00038. 27 May 2017. https://arxiv.org/abs/1706.00038

[Ven2015]

Davide Venturelli, Dominic J.J. Marchand, and Galo Roj. “Job Shop Scheduling Solver based on Quantum Annealing.” arXiv:1506.08479 29 Jun 2015 https://arxiv.org/pdf/1506.08479.pdf

[Ven2015b]

Davide Venturelli, Salvatore Mandrà, Sergey Knysh, Bryan O’Gorman, Rupak Biswas, and Vadim Smelyanskiy. “Quantum Optimization of Fully Connected Spin Glasses”. Phys. Rev. X. 18 September 2015. https://journals.aps.org/prx/abstract/10.1103/PhysRevX.5.031040

[Ven2019]

Venturelli, D., Kondratyev, A. “Reverse quantum annealing approach to portfolio optimization problems.” Quantum Mach. Intell. 1, 17–30 (2019). https://doi.org/10.1007/s42484-019-00001-w

[Vin2019]

Walter Vinci, Lorenzo Buffoni, Hossein Sadeghi, Amir Khoshaman, Evgeny Andriyash, Mohammad H. Amin. “A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealers.” 4 Dec 2019 https://arxiv.org/abs/1912.02119

[Wan2020]

Wang, B., Hu, F., Yao, H. et al. “Prime factorization algorithm based on parameter optimization of Ising model” Sci Rep 10, 7106 (2020). https://doi.org/10.1038/s41598-020-62802-5 https://www.nature.com/articles/s41598-020-62802-5.pdf

[Wat2006]

Watanabe, O., Yamamoto, M. (2006). “Average-Case Analysis for the MAX-2SAT Problem.” Biere, A., Gomes, C.P. (eds) Theory and Applications of Satisfiability Testing - SAT 2006. SAT 2006. Lecture Notes in Computer Science, vol 4121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11814948_27

[Wea2014]

S. A. Weaver, K. J. Ray, V. W. Marek, A. J. Mayer, and A. K. Walker. “Satisfiability based set membership filters.” Journal on Satisfiability, Boolean Modeling and Computation 8, 129 (2014). https://www.cs.uky.edu/~marek/papers.dir/11.dir/JSAT8_10_Weaver.pdf

[Wil2019]

D. Willsch, M. Willsch, H. De Raedt et al., “Support vector machines on the D-Wave quantum annealer.” Computer Physics Communications (2019) 107006. https://doi.org/10.1016/j.cpc.2019.107006

[Yu2021]

Sizhuo Yu and Tahar Nabil. “Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.” Front. Phys., 14 September 2021 https://www.frontiersin.org/articles/10.3389/fphy.2021.730685/full https://doi.org/10.3389/fphy.2021.730685

[Yur2022]

Alp Yurtsever, Tolga Birdal, and Vladislav Golyanik. “Q-FW: A Hybrid Classical-Quantum Frank-Wolfe for Quadratic Binary Optimization.” arXiv:2203.12633

[Zah2019]

Ehsan Zahedinejad, Daniel Crawford, Clemens Adolphs, and Jaspreet S. Oberoi. “Multi-Community Detection in Signed Graphs Using Quantum Hardware.” https://1qbit.com/our-thinking/research-papers arXiv:1901.04873 [quant-ph]

[Zbi2020]

Stefanie Zbinden, Andreas Bartschi, Hristo Djidjev & Stephan Eidenbenz . “Embedding Algorithms for Quantum Annealers with Chimera and Pegasus Connection Topologies.” Sadayappan, P., Chamberlain, B., Juckeland, G., Ltaief, H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science(), vol 12151. Springer, Cham. https://doi.org/10.1007/978-3-030-50743-5_10