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      Experimental investigation of performance differences between coherent Ising machines and a quantum annealer

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          Abstract

          Benchmarking the coherent Ising machine and the D-Wave quantum annealer sheds light on the importance of connectivity.

          Abstract

          Physical annealing systems provide heuristic approaches to solving combinatorial optimization problems. Here, we benchmark two types of annealing machines—a quantum annealer built by D-Wave Systems and measurement-feedback coherent Ising machines (CIMs) based on optical parametric oscillators—on two problem classes, the Sherrington-Kirkpatrick (SK) model and MAX-CUT. The D-Wave quantum annealer outperforms the CIMs on MAX-CUT on cubic graphs. On denser problems, however, we observe an exponential penalty for the quantum annealer [exp(–α DW N 2)] relative to CIMs [exp(–α CIM N)] for fixed anneal times, both on the SK model and on 50% edge density MAX-CUT. This leads to a several orders of magnitude time-to-solution difference for instances with over 50 vertices. An optimal–annealing time analysis is also consistent with a substantial projected performance difference. The difference in performance between the sparsely connected D-Wave machine and the fully-connected CIMs provides strong experimental support for efforts to increase the connectivity of quantum annealers.

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          Solvable Model of a Spin-Glass

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            A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem

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                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                May 2019
                24 May 2019
                : 5
                : 5
                : eaau0823
                Affiliations
                [1 ]Research Laboratory of Electronics, Massachusetts Institute of Technology, 50 Vassar Street, Cambridge, MA 02139, USA.
                [2 ]National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo 101-8403, Japan.
                [3 ]NTT Basic Research Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan.
                [4 ]E. L. Ginzton Laboratory, Stanford University, Stanford, CA 94305, USA.
                [5 ]School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA.
                [6 ]NASA Ames Research Center Quantum Artificial Intelligence Laboratory (QuAIL), Mail Stop 269-1, Moffett Field, CA 94035, USA.
                [7 ]USRA Research Institute for Advanced Computer Science (RIACS), 615 National Avenue, Mountain View, CA 94035, USA.
                [8 ]California Institute of Technology, Pasadena, CA 91125, USA.
                [9 ]NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato Wakamiya, Atsugi, Kanagawa 243-0198, Japan.
                [10 ]ImPACT Program, Japan Science and Technology Agency, Gobancho 7, Chiyoda-ku, Tokyo 102-0076, Japan.
                Author notes
                [*]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0003-4976-2236
                http://orcid.org/0000-0003-1322-8744
                http://orcid.org/0000-0002-1177-9887
                http://orcid.org/0000-0003-0452-7603
                http://orcid.org/0000-0002-0470-0050
                http://orcid.org/0000-0002-3695-1698
                http://orcid.org/0000-0002-2947-5312
                http://orcid.org/0000-0003-1182-4198
                http://orcid.org/0000-0002-2176-9842
                http://orcid.org/0000-0003-2462-0987
                http://orcid.org/0000-0002-5787-5911
                http://orcid.org/0000-0003-3667-1642
                http://orcid.org/0000-0001-7905-9424
                http://orcid.org/0000-0001-6056-4287
                http://orcid.org/0000-0003-1331-0318
                http://orcid.org/0000-0002-5512-1905
                http://orcid.org/0000-0002-5156-7678
                http://orcid.org/0000-0002-1043-3489
                http://orcid.org/0000-0003-1253-9049
                http://orcid.org/0000-0002-4150-6804
                Article
                aau0823
                10.1126/sciadv.aau0823
                6534389
                31139743
                f160a4e7-c327-407b-a468-9f75c1742332
                Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 04 May 2018
                : 17 April 2019
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: PHY-1648807
                Funded by: doi http://dx.doi.org/10.13039/100005492, Stanford University;
                Funded by: doi http://dx.doi.org/10.13039/100011040, Intelligence Community Postdoctoral Research Fellowship Program;
                Funded by: doi http://dx.doi.org/10.13039/501100002241, Japan Science and Technology Agency;
                Funded by: NASA Academic Mission Services;
                Award ID: NNA16BD14C
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Applied Physics
                Computer Science
                Applied Physics
                Custom metadata
                Eunice Diego

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