{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T01:45:29Z","timestamp":1767836729924,"version":"3.49.0"},"reference-count":267,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T00:00:00Z","timestamp":1616976000000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Laboratory Directed Research and Development Program of Oak Ridge National Laboratory"},{"name":"U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research","award":["DE-AC05-00OR22725"],"award-info":[{"award-number":["DE-AC05-00OR22725"]}]},{"DOI":"10.13039\/100016818","name":"UT-Battelle, LLC","doi-asserted-by":"crossref","award":["DE-AC0500OR22725"],"award-info":[{"award-number":["DE-AC0500OR22725"]}],"id":[{"id":"10.13039\/100016818","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Department of Energy, Office of Science, Early Career Research Program"},{"name":"ASCR Testbed Pathfinder Program at Oak Ridge National Laboratory","award":["FWP #ERKJ332"],"award-info":[{"award-number":["FWP #ERKJ332"]}]},{"name":"United States Department of Defense and used resources of the Computational Research and Development Programs at Oak Ridge National Laboratory"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Parallel Comput."],"published-print":{"date-parts":[[2020,3,31]]},"abstract":"<jats:p>Novel uses of graphical processing units for accelerated computation revolutionized the field of high-performance scientific computing by providing specialized workflows tailored to algorithmic requirements. As the era of Moore\u2019s law draws to a close, many new non\u2013von Neumann processors are emerging as potential computational accelerators, including those based on the principles of neuromorphic computing, tensor algebra, and quantum information. While development of these new processors is continuing to mature, the potential impact on accelerated computing is anticipated to be profound. We discuss how different processing models can advance computing in key scientific paradigms: machine learning and constraint satisfaction. Significantly, each of these new processor types utilizes a fundamentally different model of computation, and this raises questions about how to best use such processors in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This also hints at the infrastructure needed to integrate next-generation processing units into future high-performance computing systems.<\/jats:p>","DOI":"10.1145\/3380940","type":"journal-article","created":{"date-parts":[[2020,3,29]],"date-time":"2020-03-29T05:47:31Z","timestamp":1585460851000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Accelerating Scientific Computing in the Post-Moore\u2019s Era"],"prefix":"10.1145","volume":"7","author":[{"given":"Kathleen E.","family":"Hamilton","sequence":"first","affiliation":[{"name":"Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA"}]},{"given":"Catherine D.","family":"Schuman","sequence":"additional","affiliation":[{"name":"Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA"}]},{"given":"Steven R.","family":"Young","sequence":"additional","affiliation":[{"name":"Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA"}]},{"given":"Ryan S.","family":"Bennink","sequence":"additional","affiliation":[{"name":"Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA"}]},{"given":"Neena","family":"Imam","sequence":"additional","affiliation":[{"name":"Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA"}]},{"given":"Travis S.","family":"Humble","sequence":"additional","affiliation":[{"name":"Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,3,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog0901_7"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(96)00061-5"},{"key":"e_1_2_1_3_1","volume-title":"Vineyard","author":"Aimone James B.","year":"2019","unstructured":"James B. Aimone, William Severa, and Craig M. Vineyard. 2019. Composing neural algorithms with Fugu. arXiv preprint arXiv:1905.12130 (2019)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.90.015002"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.8.031016"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.119.110502"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2285253"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90043-9"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.55.1530"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 33rd International Conference on Machine Learning), Maria Florina Balcan and Kilian Q. Weinberger (Eds.)","volume":"48","author":"Amodei Dario","year":"2016","unstructured":"Dario Amodei, Sundaram Ananthanarayanan, Rishita Anubhai, Jingliang Bai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Qiang Cheng, Guoliang Chen, Jie Chen, Jingdong Chen, Zhijie Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Ke Ding, Niandong Du, Erich Elsen, Jesse Engel, Weiwei Fang, Linxi Fan, Christopher Fougner, Liang Gao, Caixia Gong, Awni Hannun, Tony Han, Lappi Johannes, Bing Jiang, Cai Ju, Billy Jun, Patrick LeGresley, Libby Lin, Junjie Liu, Yang Liu, Weigao Li, Xiangang Li, Dongpeng Ma, Sharan Narang, Andrew Ng, Sherjil Ozair, Yiping Peng, Ryan Prenger, Sheng Qian, Zongfeng Quan, Jonathan Raiman, Vinay Rao, Sanjeev Satheesh, David Seetapun, Shubho Sengupta, Kavya Srinet, Anuroop Sriram, Haiyuan Tang, Liliang Tang, Chong Wang, Jidong Wang, Kaifu Wang, Yi Wang, Zhijian Wang, Zhiqian Wang, Shuang Wu, Likai Wei, Bo Xiao, Wen Xie, Yan Xie, Dani Yogatama, Bin Yuan, Jun Zhan, and Zhenyao Zhu. 2016. Deep speech 2 : End-to-end speech recognition in English and Mandarin. In Proceedings of the 33rd International Conference on Machine Learning), Maria Florina Balcan and Kilian Q. Weinberger (Eds.), Vol. 48. PMLR, New York, New York, 173--182. Retrieved from http:\/\/proceedings.mlr.press\/v48\/amodei16.html."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2019.00048"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252637"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.71.066707"},{"key":"e_1_2_1_14_1","volume-title":"Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940","author":"Bello Irwan","year":"2016","unstructured":"Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio. 2016. Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940 (2016)."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41534-019-0157-8"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/ab14b5"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1088\/2058-9565\/aabd98"},{"key":"e_1_2_1_18_1","unstructured":"V. Bergholm J. Izaac M. Schuld C. Gogolin and N. Killoran. 2018. PennyLane: Automatic differentiation of hybrid quantum-classical computations. ArXiv e-prints (Nov. 2018). arxiv:quant-ph\/1811.04968"},{"key":"e_1_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Hannes Bernien Sylvain Schwartz Alexander Keesling Harry Levine Ahmed Omran Hannes Pichler Soonwon Choi Alexander S. Zibrov Manuel Endres Markus Greiner et al. 2017. Probing many-body dynamics on a 51-atom quantum simulator. Nature 551 7682 (2017) 579.","DOI":"10.1038\/nature24622"},{"key":"e_1_2_1_20_1","volume-title":"Quantum machine learning. Nature 549, 7671","author":"Biamonte Jacob","year":"2017","unstructured":"Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. 2017. Quantum machine learning. Nature 549, 7671 (2017), 195."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.76.3251"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00378-2"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00658-0"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-015-1150-6"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSPEC.2018.8389173"},{"key":"e_1_2_1_26_1","volume-title":"Quantum advantage with shallow circuits. Science 362, 6412","author":"Bravyi Sergey","year":"2018","unstructured":"Sergey Bravyi, David Gosset, and Robert Koenig. 2018. Quantum advantage with shallow circuits. Science 362, 6412 (2018), 308--311."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007651"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007651"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRC.2017.8123664"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.5088164"},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the 16th IEEE Conference on Computational Complexity. IEEE, 131--137","author":"Buhrman Harry","year":"2001","unstructured":"Harry Buhrman, Christoph Durr, Mark Heiligman, Peter Hoyer, Fr\u00e9d\u00e9ric Magniez, Miklos Santha, and Ronald De Wolf. 2001. Quantum algorithms for element distinctness. In Proceedings of the 16th IEEE Conference on Computational Complexity. IEEE, 131--137."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-006-0007-1"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065714500294"},{"key":"e_1_2_1_34_1","volume-title":"A practical heuristic for finding graph minors. arXiv preprint arXiv:1406.2741","author":"Cai Jun","year":"2014","unstructured":"Jun Cai, William G. Macready, and Aidan Roy. 2014. A practical heuristic for finding graph minors. arXiv preprint arXiv:1406.2741 (2014)."},{"key":"e_1_2_1_35_1","first-page":"191","article-title":"The human brain project and neuromorphic computing","volume":"28","author":"Calimera Andrea","year":"2013","unstructured":"Andrea Calimera, Enrico Macii, and Massimo Poncino. 2013. The human brain project and neuromorphic computing. Funct. Neurol. 28, 3 (2013), 191.","journal-title":"Funct. Neurol."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2014.6974673"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6707077"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2017.2777181"},{"key":"e_1_2_1_39_1","first-page":"10258","article-title":"Quantum annealing for systems of polynomial equations. Sci","volume":"9","author":"Chang Chia Cheng","year":"2019","unstructured":"Chia Cheng Chang, Arjun Gambhir, Travis S. Humble, and Shigetoshi Sota. 2019. Quantum annealing for systems of polynomial equations. Sci. Rep. 9, 1 (2019), 10258.","journal-title":"Rep."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-008-0082-9"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-010-0200-3"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1103\/physreva.96.043850"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2017.8"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystems.2006.09.022"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02551274"},{"key":"e_1_2_1_46_1","unstructured":"D-WAVE. 2018. D-WAVE Publications. Retrieved from http:\/\/www.dwavesys.com\/resources\/publications."},{"key":"e_1_2_1_47_1","unstructured":"E. D. Dahl. 2013. Programming with D-Wave: Map Coloring Problem. Technical Report. D-Wave Systems. Retrieved from https:\/\/www.dwavesys.com\/resources\/publications."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.98.012324"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2015.7169244"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"e_1_2_1_51_1","first-page":"11","article-title":"PyNN: A common interface for neuronal network simulators","volume":"2","author":"Davison Andrew P.","year":"2009","unstructured":"Andrew P. Davison, Daniel Br\u00fcderle, Jochen M. Eppler, Jens Kremkow, Eilif Muller, Dejan Pecevski, Laurent Perrinet, and Pierre Yger. 2009. PyNN: A common interface for neuronal network simulators. Front. Neuroinf. 2 (2009), 11.","journal-title":"Front. Neuroinf."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46687-3_21"},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of the 14th International Conference on Computational Science and Its Applications (ICCSA\u201914)","author":"Mota de Oliveira Jo\u00e3o Eliakin","unstructured":"Jo\u00e3o Eliakin Mota de Oliveira and Marcos G. Quiles. 2014. Community detection in complex networks using coupled Kuramoto oscillators. In Proceedings of the 14th International Conference on Computational Science and Its Applications (ICCSA\u201914). IEEE, 85--90."},{"key":"e_1_2_1_54_1","volume-title":"Demonstration of a small programmable quantum computer with atomic qubits. Nature 536, 7614","author":"Debnath Shantanu","year":"2016","unstructured":"Shantanu Debnath, Norbert M. Linke, Caroline Figgatt, Kevin A. Landsman, Kevin Wright, and Christopher Monroe. 2016. Demonstration of a small programmable quantum computer with atomic qubits. Nature 536, 7614 (2016), 63."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.89.035002"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.6.031015"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","unstructured":"N. G. Dickson M. W. Johnson M. H. Amin R. Harris F. Altomare A. J. Berkley P. Bunyk J. Cai E. M. Chapple P. Chavez F. Cioata T. Cirip P. deBuen M. Drew-Brook C. Enderud S. Gildert F. Hamze J. P. Hilton E. Hoskinson K. Karimi E. Ladizinsky N. Ladizinsky T. Lanting T. Mahon R. Neufeld T. Oh I. Perminov C. Petroff A. Przybysz C. Rich P. Spear A. Tcaciuc M. C. Thom E. Tolkacheva S. Uchaikin J. Wang A. B. Wilson Z. Merali and G. Rose. 2013. Thermally assisted quantum annealing of a 16-qubit problem. Nat. Commun. 4 (21 05 2013) 1903. Retrieved from http:\/\/dx.doi.org\/10.1038\/ncomms2920.","DOI":"10.1038\/ncomms2920"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRC.2016.7738691"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24318-4_9"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6633\/aab406"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.8.031027"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6706746"},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems. 1117--1125","author":"Esser Steve K.","unstructured":"Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, and Dharmendra S. Modha. 2015. Backpropagation for energy-efficient neuromorphic computing. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 1117--1125."},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1604850113"},{"key":"e_1_2_1_65_1","volume-title":"Proceedings of the ACM\/IEEE Supercomputing Conference. IEEE, 47--47","author":"Fan Zhe","year":"2004","unstructured":"Zhe Fan, Feng Qiu, Arie Kaufman, and Suzanne Yoakum-Stover. 2004. GPU cluster for high performance computing. In Proceedings of the ACM\/IEEE Supercomputing Conference. IEEE, 47--47."},{"key":"e_1_2_1_66_1","volume-title":"A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028","author":"Farhi Edward","year":"2014","unstructured":"Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. 2014. A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028 (2014)."},{"key":"e_1_2_1_67_1","volume-title":"A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, 5516","author":"Farhi Edward","year":"2001","unstructured":"Edward Farhi, Jeffrey Goldstone, Sam Gutmann, Joshua Lapan, Andrew Lundgren, and Daniel Preda. 2001. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, 5516 (2001), 472--475."},{"key":"e_1_2_1_68_1","volume-title":"Quantum algorithms for fixed qubit architectures. arXiv preprint arXiv:1703.06199","author":"Farhi Edward","year":"2017","unstructured":"Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Hartmut Neven. 2017. Quantum algorithms for fixed qubit architectures. arXiv preprint arXiv:1703.06199 (2017)."},{"key":"e_1_2_1_69_1","volume-title":"Quantum computation by adiabatic evolution. arXiv preprint quant-ph\/0001106","author":"Farhi Edward","year":"2000","unstructured":"Edward Farhi, Jeffrey Goldstone, Sam Gutmann, and Michael Sipser. 2000. Quantum computation by adiabatic evolution. arXiv preprint quant-ph\/0001106 (2000)."},{"key":"e_1_2_1_70_1","volume-title":"Harrow","author":"Farhi Edward","year":"2016","unstructured":"Edward Farhi and Aram W. Harrow. 2016. Quantum supremacy through the quantum approximate optimization algorithm. arXiv preprint arXiv:1602.07674 (2016)."},{"key":"e_1_2_1_71_1","volume-title":"Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002","author":"Farhi Edward","year":"2018","unstructured":"Edward Farhi and Hartmut Neven. 2018. Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002 (2018)."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-1809.1936.tb02137.x"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00714"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2009.11.002"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevApplied.8.024030"},{"key":"e_1_2_1_76_1","unstructured":"Fujitsu. 2018. Fujitsu Quantum-inspired Computing Digital Annealer. Retrieved from http:\/\/www.fujitsu.com\/global\/digitalannealer\/."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2014.2304638"},{"key":"e_1_2_1_78_1","volume-title":"Quantum advantage for the LOCAL model in distributed computing. arXiv preprint arXiv:1810.10838","author":"Gall Fran\u00e7ois Le","year":"2018","unstructured":"Fran\u00e7ois Le Gall, Harumichi Nishimura, and Ansis Rosmanis. 2018. Quantum advantage for the LOCAL model in distributed computing. arXiv preprint arXiv:1810.10838 (2018)."},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41534-019-0149-8"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975482.87"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.41.1843"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-018-1863-4"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41534-018-0116-9"},{"key":"e_1_2_1_84_1","volume-title":"An initialization strategy for addressing barren plateaus in parametrized quantum circuits. arXiv preprint arXiv:1903.05076","author":"Grant Edward","year":"2019","unstructured":"Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti. 2019. An initialization strategy for addressing barren plateaus in parametrized quantum circuits. arXiv preprint arXiv:1903.05076 (2019)."},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2146789"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/237814.237866"},{"key":"e_1_2_1_87_1","volume-title":"Robert L. Byer, Martin M. Fejer, Hideo Mabuchi, Dirk Englund, Eleanor Rieffel, Hiroki Takesue, and Yoshihisa Yamamoto.","author":"Hamerly Ryan","year":"2019","unstructured":"Ryan Hamerly, Takahiro Inagaki, Peter L. McMahon, Davide Venturelli, Alireza Marandi, Tatsuhiro Onodera, Edwin Ng, Carsten Langrock, Kensuke Inaba, Toshimori Honjo, Koji Enbutsu, Takeshi Umeki, Ryoichi Kasahara, Shoko Utsunomiya, Satoshi Kako, Ken ichi Kawarabayashi, Robert L. Byer, Martin M. Fejer, Hideo Mabuchi, Dirk Englund, Eleanor Rieffel, Hiroki Takesue, and Yoshihisa Yamamoto. 2019. Experimental investigation of performance differences between Coherent Ising Machines and a quantum annealer. Science Advances 5, 5 (2019)."},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.99.062323"},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-016-1513-7"},{"key":"e_1_2_1_90_1","volume-title":"Humble","author":"Hamilton Kathleen E.","year":"2018","unstructured":"Kathleen E. Hamilton and Travis S. Humble. 2018. Spiking spin-glass models for label propagation and community detection. arXiv preprint arXiv:1801.03571 (2018)."},{"key":"e_1_2_1_91_1","volume-title":"Proceedings of the Neuromorphic Computing Symposium. ACM, 9.","author":"Hamilton Kathleen E.","unstructured":"Kathleen E. Hamilton, Neena Imam, and Travis S. Humble. 2017. Community detection with spiking neural networks for neuromorphic hardware. In Proceedings of the Neuromorphic Computing Symposium. ACM, 9."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1145\/3223048"},{"key":"e_1_2_1_93_1","volume-title":"Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW\u201918)","author":"Hamilton Kathleen E.","unstructured":"Kathleen E. Hamilton, Catherine D. Schuman, Steven R. Young, Neena Imam, and Travis S. Humble. 2018. Neural networks and graph algorithms with next-generation processors. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW\u201918). IEEE, 1194--1203."},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.3390\/e18040151"},{"key":"e_1_2_1_95_1","doi-asserted-by":"crossref","unstructured":"R. Harris Y. Sato A. J. Berkley M. Reis F. Altomare M. H. Amin K. Boothby P. Bunyk C. Deng C. Enderud et al. 2018. Phase transitions in a programmable quantum spin glass simulator. Science 361 6398 (2018) 162--165.","DOI":"10.1126\/science.aat2025"},{"key":"e_1_2_1_96_1","volume-title":"Gambetta","author":"Havl\u00ed\u010dek Vojt\u011bch","year":"2019","unstructured":"Vojt\u011bch Havl\u00ed\u010dek, Antonio D. C\u00f3rcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta. 2019. Supervised learning with quantum-enhanced feature spaces. Nature 567, 7747 (2019), 209."},{"key":"e_1_2_1_97_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_2_1_98_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevApplied.5.034007"},{"key":"e_1_2_1_99_1","volume-title":"Ping Tak Peter Tang, and Alexander Heinecke","author":"Henry Greg","year":"2019","unstructured":"Greg Henry, Ping Tak Peter Tang, and Alexander Heinecke. 2019. Leveraging the bfloat16 artificial intelligence datatype for higher-precision computations. CoRR abs\/1904.06376 (2019)."},{"key":"e_1_2_1_100_1","volume-title":"Palmer","author":"Hertz John","year":"1991","unstructured":"John Hertz, Anders Krogh, and Richard G. Palmer. 1991. Introduction to the Theory of Neural Computation. Santa Fe Institute Studies in the Sciences of Complexity, Vol. 1. Addison-Wesley."},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1145\/2512329"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760128018"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.79.8.2554"},{"key":"e_1_2_1_106_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.81.10.3088"},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.5555\/2730034.2730138"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1080\/14786435.2011.616547"},{"key":"e_1_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247968"},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1088\/1749-4680\/7\/1\/015006"},{"key":"e_1_2_1_112_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2016.113"},{"key":"e_1_2_1_113_1","volume-title":"Britt","author":"Humble Travis S.","year":"2018","unstructured":"Travis S. Humble, Ronald J. Sadlier, and Keith A. Britt. 2018. Simulated execution of hybrid quantum computing systems. In Proceedings of the Quantum Information Science, Sensing, and Computation X, Vol. 10660. International Society for Optics and Photonics, 1066002."},{"key":"e_1_2_1_114_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDAT.2019.2907130"},{"key":"e_1_2_1_115_1","unstructured":"IBM. 2018. IBM Q-Experience. Retrieved from http:\/\/www.research.ibm.com\/ibm-q\/."},{"key":"e_1_2_1_116_1","unstructured":"IBM. 2018. Qiskit Aqua. Retrieved from https:\/\/qiskit.org\/aqua."},{"key":"e_1_2_1_117_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aah4243"},{"key":"e_1_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1145\/3087801.3087811"},{"key":"e_1_2_1_119_1","first-page":"17667","article-title":"Quantum annealing for prime factorization. Sci","volume":"8","author":"Jiang Shuxian","year":"2018","unstructured":"Shuxian Jiang, Keith A. Britt, Alexander J. McCaskey, Travis S. Humble, and Sabre Kais. 2018. Quantum annealing for prime factorization. Sci. Rep. 8, 1 (2018), 17667.","journal-title":"Rep."},{"key":"e_1_2_1_120_1","doi-asserted-by":"publisher","unstructured":"M. W. Johnson M. H. S. Amin S. Gildert T. Lanting F. Hamze N. Dickson R. Harris A. J. Berkley J. Johansson P. Bunyk E. M. Chapple C. Enderud J. P. Hilton K. Karimi E. Ladizinsky N. Ladizinsky T. Oh I. Perminov C. Rich M. C. Thom E. Tolkacheva C. J. S. Truncik S. Uchaikin J. Wang B. Wilson and G. Rose. 2011. Quantum annealing with manufactured spins. Nature 473 7346 (12 05 2011) 194--198. Retrieved from http:\/\/dx.doi.org\/10.1038\/nature10012.","DOI":"10.1038\/nature10012"},{"key":"e_1_2_1_121_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00118"},{"key":"e_1_2_1_122_1","unstructured":"Steven Jordan. 2018. Quantum Algorithm Zoo. Retrieved from https:\/\/math.nist.gov\/quantum\/zoo\/."},{"key":"e_1_2_1_123_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.95.050501"},{"key":"e_1_2_1_124_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.032271057"},{"key":"e_1_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"e_1_2_1_126_1","volume-title":"Gambetta","author":"Kandala Abhinav","year":"2017","unstructured":"Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M. Chow, and Jay M. Gambetta. 2017. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature 549, 7671 (2017), 242."},{"key":"e_1_2_1_127_1","volume-title":"Gambetta","author":"Kandala Abhinav","year":"2019","unstructured":"Abhinav Kandala, Kristan Temme, Antonio D. C\u00f3rcoles, Antonio Mezzacapo, Jerry M. Chow, and Jay M. Gambetta. 2019. Error mitigation extends the computational reach of a noisy quantum processor. Nature 567, 7749 (2019), 491."},{"key":"e_1_2_1_128_1","volume-title":"Sparse Distributed Memory","author":"Kanerva Pentti","unstructured":"Pentti Kanerva. 1988. Sparse Distributed Memory. The MIT Press."},{"key":"e_1_2_1_129_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.11.014"},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.4.021008"},{"key":"e_1_2_1_131_1","volume-title":"APS Meet. Abstr. Article A42","author":"Kelly J.","year":"2019","unstructured":"J. Kelly, Z. Chen, B. Chiaro, B. Foxen, J. Martinis, and Google Quantum Hardware Team. 2019. Operating and characterizing of a 72 superconducting qubit processor \u201cBristlecone\u201d: Part 1. In APS Meet. Abstr. Article A42.002, A42.002 pages."},{"key":"e_1_2_1_132_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLUSTR.2009.5289128"},{"key":"e_1_2_1_133_1","unstructured":"Andrew D. King William Bernoudy James King Andrew J. Berkley and Trevor Lanting. [n.d.]. Emulating the coherent Ising machine with a mean-field algorithm. arXiv:quant-ph\/1806.08422v1."},{"key":"e_1_2_1_134_1","doi-asserted-by":"crossref","unstructured":"Andrew D. King Juan Carrasquilla Jack Raymond Isil Ozfidan Evgeny Andriyash Andrew Berkley Mauricio Reis Trevor Lanting Richard Harris Fabio Altomare et al. 2018. Observation of topological phenomena in a programmable lattice of 1 800 qubits. Nature 560 7719 (2018) 456.","DOI":"10.1038\/s41586-018-0410-x"},{"key":"e_1_2_1_135_1","volume-title":"Performance of a quantum annealer on range-limited constraint satisfaction problems. arXiv preprint arXiv:1502.02098","author":"King Andrew D.","year":"2015","unstructured":"Andrew D. King, Trevor Lanting, and Richard Harris. 2015. Performance of a quantum annealer on range-limited constraint satisfaction problems. arXiv preprint arXiv:1502.02098 (2015)."},{"key":"e_1_2_1_136_1","volume-title":"Jeremy P. Hilton, and Catherine C. McGeoch.","author":"King James","year":"2017","unstructured":"James King, Sheir Yarkoni, Jack Raymond, Isil Ozfidan, Andrew D. King, Mayssam Mohammadi Nevisi, Jeremy P. Hilton, and Catherine C. McGeoch. 2017. Quantum annealing amid local ruggedness and global frustration. arXiv preprint arXiv:1701.04579 (2017)."},{"key":"e_1_2_1_137_1","volume-title":"Vecchi","author":"Kirkpatrick Scott","year":"1983","unstructured":"Scott Kirkpatrick, C. Daniel Gelatt, and Mario P. Vecchi. 1983. Optimization by simulated annealing. Science 220, 4598 (1983), 671--680."},{"key":"e_1_2_1_138_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-013-0683-9"},{"key":"e_1_2_1_139_1","doi-asserted-by":"publisher","DOI":"10.1109\/21.87054"},{"key":"e_1_2_1_140_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.5089550"},{"key":"e_1_2_1_141_1","volume-title":"Optics in Our Time","author":"Krenn Mario","unstructured":"Mario Krenn, Mehul Malik, Thomas Scheidl, Rupert Ursin, and Anton Zeilinger. 2016. Quantum communication with photons. In Optics in Our Time. Springer, 455--482."},{"key":"e_1_2_1_142_1","volume-title":"Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Front. Neurosci. 9","author":"Kudithipudi Dhireesha","year":"2015","unstructured":"Dhireesha Kudithipudi, Qutaiba Saleh, Cory Merkel, James Thesing, and Bryant Wysocki. 2015. Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Front. Neurosci. 9 (2015)."},{"key":"e_1_2_1_143_1","volume-title":"Proceedings of the IEEE\/ACM International Symposium on Nanoscale Architectures (NANOARCH\u201912)","author":"Manjari","unstructured":"Manjari S. Kulkarni and Christof Teuscher. 2012. Memristor-based reservoir computing. In Proceedings of the IEEE\/ACM International Symposium on Nanoscale Architectures (NANOARCH\u201912). IEEE, 226--232."},{"key":"e_1_2_1_144_1","doi-asserted-by":"publisher","DOI":"10.5555\/140370.140377"},{"key":"e_1_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjst\/e2015-02344-2"},{"key":"e_1_2_1_146_1","doi-asserted-by":"publisher","DOI":"10.1145\/3212734.3212744"},{"key":"e_1_2_1_147_1","volume-title":"Deep learning. Nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521, 7553 (2015), 436--444."},{"key":"e_1_2_1_148_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_1_149_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2006.878786"},{"key":"e_1_2_1_150_1","volume-title":"Proceedings of the 26th International Conference on Machine Learning. ACM, 609--616","author":"Lee Honglak","unstructured":"Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. 2009. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th International Conference on Machine Learning. ACM, 609--616."},{"key":"e_1_2_1_151_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772755"},{"key":"e_1_2_1_152_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.7.021050"},{"key":"e_1_2_1_153_1","volume-title":"Programming spiking neural networks on Intel Loihi. Computer 51, 3","author":"Lin Chit-Kwan","year":"2018","unstructured":"Chit-Kwan Lin, Andreas Wild, Gautham Chinya, Mike Davies, Narayan Srinivasa, Dan Lavery, and Hong Wang. 2018. Programming spiking neural networks on Intel Loihi. Computer 51, 3 (2018)."},{"key":"e_1_2_1_154_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1618020114"},{"key":"e_1_2_1_155_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.98.062324"},{"key":"e_1_2_1_156_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.121.040502"},{"key":"e_1_2_1_157_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2014.00005"},{"key":"e_1_2_1_158_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"e_1_2_1_159_1","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(85)90041-4"},{"key":"e_1_2_1_160_1","doi-asserted-by":"publisher","DOI":"10.1038\/nphoton.2014.249"},{"key":"e_1_2_1_161_1","volume-title":"Next steps in quantum computing: Computer science\u2019s role. arXiv preprint arXiv:1903.10541","author":"Martonosi Margaret","year":"2019","unstructured":"Margaret Martonosi and Martin Roetteler. 2019. Next steps in quantum computing: Computer science\u2019s role. arXiv preprint arXiv:1903.10541 (2019)."},{"key":"e_1_2_1_162_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2018.07.007"},{"key":"e_1_2_1_163_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-07090-4"},{"key":"e_1_2_1_164_1","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/18\/2\/023023"},{"key":"e_1_2_1_165_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1987.1057328"},{"key":"e_1_2_1_166_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aah5178"},{"key":"e_1_2_1_167_1","doi-asserted-by":"crossref","unstructured":"Paul A. Merolla John V. Arthur Rodrigo Alvarez-Icaza Andrew S. Cassidy Jun Sawada Filipp Akopyan Bryan L. Jackson Nabil Imam Chen Guo Yutaka Nakamura et al. 2014. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345 6197 (2014) 668--673.","DOI":"10.1126\/science.1254642"},{"key":"e_1_2_1_168_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.98.032309"},{"key":"e_1_2_1_169_1","volume-title":"Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP\u201911)","author":"Sainath Tara N.","unstructured":"Abdel-rahman Mohamed, Tara N. Sainath, George Dahl, Bhuvana Ramabhadran, Geoffrey E. Hinton, and Michael A. Picheny. 2011. Deep belief networks using discriminative features for phone recognition. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP\u201911). IEEE, 5060--5063."},{"key":"e_1_2_1_170_1","doi-asserted-by":"publisher","DOI":"10.1088\/2058-9565\/aab822"},{"key":"e_1_2_1_171_1","doi-asserted-by":"publisher","DOI":"10.1038\/npjqi.2015.23"},{"key":"e_1_2_1_172_1","unstructured":"C. Moore and S. Mertens. 2011. The Nature of Computation. OUP Oxford. Retrieved from https:\/\/books.google.com\/books?id&equals;5M8le2uAIC8C."},{"key":"e_1_2_1_173_1","doi-asserted-by":"publisher","DOI":"10.1088\/2058-9565\/aac869"},{"key":"e_1_2_1_174_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.95.032323"},{"key":"e_1_2_1_175_1","volume-title":"Garrett Rose, Catherine D. Schuman, Alex Belianinov, C. Patrick Collier, and Stephen A. Sarles.","author":"Najem Joseph S.","year":"2018","unstructured":"Joseph S. Najem, Graham J. Taylor, Ryan J. Weiss, Md Sakib Hasan, Garrett Rose, Catherine D. Schuman, Alex Belianinov, C. Patrick Collier, and Stephen A. Sarles. 2018. Memristive ion channel-doped biomembranes as synaptic mimics. ACS Nano 12, 5 (2018)."},{"key":"e_1_2_1_176_1","volume-title":"Proc. of TELEMATIK 8, LNMC-ARTICLE-2002-005","author":"Natschl\u00e4ger Thomas","year":"2002","unstructured":"Thomas Natschl\u00e4ger, Wolfgang Maass, and Henry Markram. 2002. The \u201cliquid computer\u201d: A novel strategy for real-time computing on time series. Spec. Issue Found. Inf. Proc. of TELEMATIK 8, LNMC-ARTICLE-2002-005 (2002), 39--43."},{"key":"e_1_2_1_177_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00324"},{"key":"e_1_2_1_178_1","unstructured":"Nengo. 2018. The Nengo Neural Simulator. Retrieved from https:\/\/www.nengo.ai\/."},{"key":"e_1_2_1_179_1","volume-title":"Sheir Yarkoni, and Bob Parney.","author":"Neukart Florian","year":"2017","unstructured":"Florian Neukart, Gabriele Compostella, Christian Seidel, David Von Dollen, Sheir Yarkoni, and Bob Parney. 2017. Optimizing traffic flow using quantum annealing and classical machine learning. arXiv preprint arXiv:1708.01625 (2017)."},{"key":"e_1_2_1_180_1","volume-title":"Macready","author":"Neven Hartmut","year":"2008","unstructured":"Hartmut Neven, Geordie Rose, and William G. Macready. 2008. Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization. arXiv preprint arXiv:0804.4457 (2008)."},{"key":"e_1_2_1_181_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRC.2017.8123653"},{"key":"e_1_2_1_182_1","volume-title":"Chuang","author":"Nielsen Michael A.","year":"2010","unstructured":"Michael A. Nielsen and Isaac L. Chuang. 2010. Quantum Computation and Quantum Information. Cambridge University Press."},{"key":"e_1_2_1_183_1","volume-title":"Journal of Physics: Conference Series","volume":"681","author":"Novotny M. A.","year":"2005","unstructured":"M. A. Novotny, Q. L. Hobl, J. S. Hall, and K. Michielsen. 2016. Spanning tree calculations on d-Wave 2 machines. In Journal of Physics: Conference Series, Vol. 681. IOP Publishing, 012005."},{"key":"e_1_2_1_184_1","volume-title":"Fast Teaching of Boltzmann Machines with Local Inhibition","author":"Osborn Thomas R.","unstructured":"Thomas R. Osborn. 1990. Fast Teaching of Boltzmann Machines with Local Inhibition. Springer Netherlands, Dordrecht, 785--785."},{"key":"e_1_2_1_185_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.6.031007"},{"key":"e_1_2_1_186_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2013.2259038"},{"key":"e_1_2_1_187_1","volume-title":"Reinhardt","author":"Pakin Scott","year":"2018","unstructured":"Scott Pakin and Steven P. Reinhardt. 2018. A survey of programming tools for D-wave quantum-annealing processors. In Proceedings of the International Conference on High Performance Computing. Springer, 103--122."},{"key":"e_1_2_1_188_1","volume-title":"Benchmarking adiabatic quantum optimization for complex network analysis. arXiv preprint arXiv:1604.00319","author":"Parekh Ojas","year":"2016","unstructured":"Ojas Parekh, Jeremy Wendt, Luke Shulenburger, Andrew Landahl, Jonathan Moussa, and John Aidun. 2016. Benchmarking adiabatic quantum optimization for complex network analysis. arXiv preprint arXiv:1604.00319 (2016)."},{"key":"e_1_2_1_189_1","volume-title":"Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN\u201905)","volume":"4","author":"Pavlidis N. G.","unstructured":"N. G. Pavlidis, O. K. Tasoulis, Vassilis P. Plagianakos, G. Nikiforidis, and M. N. Vrahatis. 2005. Spiking neural network training using evolutionary algorithms. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN\u201905), Vol. 4. IEEE, 2190--2194."},{"key":"e_1_2_1_190_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2016.2539352"},{"key":"e_1_2_1_191_1","doi-asserted-by":"publisher","DOI":"10.1088\/2058-9565\/aab859"},{"key":"e_1_2_1_192_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevApplied.12.014004"},{"key":"e_1_2_1_193_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2010.05.001"},{"key":"e_1_2_1_194_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5213"},{"key":"e_1_2_1_195_1","volume-title":"Proceedings of the IEEE International Conference on Rebooting Computing (ICRC\u201917)","author":"Plank James S.","unstructured":"James S. Plank, Garrett S. Rose, Mark E. Dean, Catherine D. Schuman, and Nathaniel C. Cady. 2017. A unified hardware\/software co-design framework for neuromorphic computing devices and applications. In Proceedings of the IEEE International Conference on Rebooting Computing (ICRC\u201917). IEEE, 1--8."},{"key":"e_1_2_1_196_1","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2013.00098"},{"key":"e_1_2_1_197_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLHPC.2016.009"},{"key":"e_1_2_1_198_1","volume-title":"Quantum computing in the NISQ era and beyond. arXiv preprint arXiv:1801.00862","author":"Preskill John","year":"2018","unstructured":"John Preskill. 2018. Quantum computing in the NISQ era and beyond. arXiv preprint arXiv:1801.00862 (2018)."},{"key":"e_1_2_1_199_1","volume-title":"Strukov","author":"Prezioso Mirko","year":"2015","unstructured":"Mirko Prezioso, Farnood Merrikh-Bayat, B. D. Hoskins, G. C. Adam, Konstantin K. Likharev, and Dmitri B. Strukov. 2015. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 7550 (2015), 61."},{"key":"e_1_2_1_200_1","unstructured":"PsiQuantum. 2019. PsiQ. Retrieved from http:\/\/psiquantum.com\/."},{"key":"e_1_2_1_201_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2010.5596809"},{"key":"e_1_2_1_202_1","volume-title":"Proceedings of the International Joint Conference on Neural Networks (IJCNN\u201913)","author":"Quiles Marcos G.","unstructured":"Marcos G. Quiles, Ezequiel R. Zorzal, and Elbert E. N. Macau. 2013. A dynamical model for community detection in complex networks. In Proceedings of the International Joint Conference on Neural Networks (IJCNN\u201913). IEEE, 1--8."},{"key":"e_1_2_1_203_1","volume-title":"Proceedings of the 26th International Conference on Machine Learning. ACM, 873--880","author":"Raina Rajat","unstructured":"Rajat Raina, Anand Madhavan, and Andrew Y. Ng. 2009. Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th International Conference on Machine Learning. ACM, 873--880."},{"key":"e_1_2_1_204_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.113.130503"},{"key":"e_1_2_1_205_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.93.218701"},{"key":"e_1_2_1_206_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.74.016110"},{"key":"e_1_2_1_207_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-014-0892-x"},{"key":"e_1_2_1_208_1","unstructured":"Rigetti. 2018. The Rigetti QPU. Retrieved from http:\/\/www.rigetti.com\/qpu."},{"key":"e_1_2_1_209_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41534-017-0017-3"},{"key":"e_1_2_1_210_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.81.046114"},{"key":"e_1_2_1_211_1","doi-asserted-by":"crossref","unstructured":"Frank Rosenblatt. 1961. Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms. Technical Report. Cornell Aeronautical Lab Inc Buffalo NY.","DOI":"10.21236\/AD0256582"},{"key":"e_1_2_1_212_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2007.4379981"},{"key":"e_1_2_1_213_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2008.03-07-486"},{"key":"e_1_2_1_214_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.100.012326"},{"key":"e_1_2_1_215_1","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/16\/4\/045006"},{"key":"e_1_2_1_216_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.96.062330"},{"key":"e_1_2_1_217_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-017-0023-6"},{"key":"e_1_2_1_218_1","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI\u201995)","author":"Schiex Thomas","year":"1995","unstructured":"Thomas Schiex, Helene Fargier, Gerard Verfaillie, et al. 1995. Valued constraint satisfaction problems: Hard and easy problems. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI\u201995). 631--639."},{"key":"e_1_2_1_219_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_2_1_220_1","doi-asserted-by":"publisher","DOI":"10.5555\/2793723.2793890"},{"key":"e_1_2_1_221_1","doi-asserted-by":"publisher","DOI":"10.3390\/e19090500"},{"key":"e_1_2_1_222_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.99.032331"},{"key":"e_1_2_1_223_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.101.032308"},{"key":"e_1_2_1_224_1","doi-asserted-by":"publisher","DOI":"10.5555\/2684509.2684546"},{"key":"e_1_2_1_225_1","doi-asserted-by":"publisher","DOI":"10.1080\/00107514.2014.964942"},{"key":"e_1_2_1_226_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physleta.2014.11.061"},{"key":"e_1_2_1_227_1","volume-title":"Proceedings of the Neuro-Inspired Computational Elements (NICE\u201919)","author":"Schuman Catherine D.","unstructured":"Catherine D. Schuman, Kathleen Hamilton, Tiffany Mintz, Md Musabbir Adnan, Bon Woong Ku, Sung-Kyu Lim, and Garrett S. Rose. 2019. Shortest path and neighborhood subgraph extraction on a spiking memristive neuromorphic implementation. In Proceedings of the Neuro-Inspired Computational Elements (NICE\u201919) Workshop."},{"key":"e_1_2_1_228_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2016.7727192"},{"key":"e_1_2_1_229_1","volume-title":"Plank","author":"Schuman Catherine D.","year":"2017","unstructured":"Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, and James S. Plank. 2017. A survey of neuromorphic computing and neural networks in hardware. arXiv preprint arXiv:1705.06963 (2017)."},{"key":"e_1_2_1_230_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2014.00079"},{"key":"e_1_2_1_231_1","volume-title":"Proceedings of the IEEE Custom Integrated Circuits Conference (CICC\u201911)","author":"Brezzo Bernard","year":"2011","unstructured":"Jae-sun Seo, Bernard Brezzo, Yong Liu, Benjamin D. Parker, Steven K. Esser, Robert K. Montoye, Bipin Rajendran, Jos\u00e9 A. Tierno, Leland Chang, Dharmendra S. Modha, et al. 2011. A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons. In Proceedings of the IEEE Custom Integrated Circuits Conference (CICC\u201911). IEEE, 1--4."},{"key":"e_1_2_1_232_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-018-0015-y"},{"key":"e_1_2_1_233_1","volume-title":"Proceedings of the Neuro Inspired Computational Elements Workshop.","author":"Severa William M.","unstructured":"William M. Severa, Craig M. Vineyard, Ryan Dellana, and James B. Aimone. 2018. Whetstone: An accessible, platform-independent method for training spiking deep neural networks for neuromorphic processors. In Proceedings of the Neuro Inspired Computational Elements Workshop."},{"key":"e_1_2_1_234_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevApplied.7.034013"},{"key":"e_1_2_1_235_1","volume-title":"Community detection across emerging quantum architectures. arXiv preprint arXiv:1810.07765","author":"Shaydulin Ruslan","year":"2018","unstructured":"Ruslan Shaydulin, Hayato Ushijima-Mwesigwa, Ilya Safro, Susan Mniszewski, and Yuri Alexeev. 2018. Community detection across emerging quantum architectures. arXiv preprint arXiv:1810.07765 (2018)."},{"key":"e_1_2_1_236_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.35.1792"},{"key":"e_1_2_1_237_1","doi-asserted-by":"publisher","DOI":"10.1103\/physreva.96.053833"},{"key":"e_1_2_1_238_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0036144598347011"},{"key":"e_1_2_1_239_1","volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems. 1412--1421","author":"Shrestha Sumit Bam","year":"2018","unstructured":"Sumit Bam Shrestha and Garrick Orchard. 2018. SLAYER: Spike layer error reassignment in time. In Proceedings of the International Conference on Advances in Neural Information Processing Systems. 1412--1421."},{"key":"e_1_2_1_240_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1021376718708"},{"key":"e_1_2_1_241_1","volume-title":"Quantum generative adversarial network for generating discrete data. arXiv preprint arXiv:1807.01235","author":"Situ Haozhen","year":"2018","unstructured":"Haozhen Situ, Zhimin He, Lvzhou Li, and Shenggen Zheng. 2018. Quantum generative adversarial network for generating discrete data. arXiv preprint arXiv:1807.01235 (2018)."},{"key":"e_1_2_1_242_1","volume-title":"Zeng","author":"Smith Robert S.","year":"2016","unstructured":"Robert S. Smith, Michael J. Curtis, and William J. Zeng. 2016. A practical quantum instruction set architecture. arXiv preprint arXiv:1608.03355v2 (2017)."},{"key":"e_1_2_1_243_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(01)00064-8"},{"key":"e_1_2_1_244_1","doi-asserted-by":"publisher","DOI":"10.1145\/2304576.2304625"},{"key":"e_1_2_1_245_1","doi-asserted-by":"publisher","DOI":"10.1038\/78829"},{"key":"e_1_2_1_246_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(99)00038-6"},{"key":"e_1_2_1_247_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2015.7280625"},{"key":"e_1_2_1_248_1","volume-title":"An artificial neuron implemented on an actual quantum processor. arXiv preprint arXiv:1811.02266","author":"Tacchino Francesco","year":"2018","unstructured":"Francesco Tacchino, Chiara Macchiavello, Dario Gerace, and Daniele Bajoni. 2018. An artificial neuron implemented on an actual quantum processor. arXiv preprint arXiv:1811.02266 (2018)."},{"key":"e_1_2_1_249_1","doi-asserted-by":"publisher","DOI":"10.1103\/physreva.92.043821"},{"key":"e_1_2_1_250_1","doi-asserted-by":"publisher","DOI":"10.1088\/2058-9565\/aa923b"},{"key":"e_1_2_1_251_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.119.180509"},{"key":"e_1_2_1_252_1","volume-title":"Mniszewski","author":"Ushijima-Mwesigwa Hayato","year":"2017","unstructured":"Hayato Ushijima-Mwesigwa, Christian F. A. Negre, and Susan M. Mniszewski. 2017. Graph partitioning using quantum annealing on the D-wave system. arXiv preprint arXiv:1705.03082 (2017)."},{"key":"e_1_2_1_253_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00291"},{"key":"e_1_2_1_254_1","volume-title":"Van Laarhoven and Emile H. L. Aarts","author":"Peter J.","year":"1987","unstructured":"Peter J. M. Van Laarhoven and Emile H. L. Aarts. 1987. Simulated annealing. In Simulated Annealing: Theory and Applications. Springer, 7--15."},{"key":"e_1_2_1_255_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.5.031040"},{"key":"e_1_2_1_256_1","volume-title":"Kamil Br\u00e1dler, and Nathan Killoran.","author":"Verdon Guillaume","year":"2019","unstructured":"Guillaume Verdon, Juan Miguel Arrazola, Kamil Br\u00e1dler, and Nathan Killoran. 2019. A quantum approximate optimization algorithm for continuous problems. arXiv preprint arXiv:1902.00409 (2019)."},{"key":"e_1_2_1_257_1","volume-title":"A quantum algorithm to train neural networks using low-depth circuits. arXiv preprint arXiv:1712.05304","author":"Verdon Guillaume","year":"2017","unstructured":"Guillaume Verdon, Michael Broughton, and Jacob Biamonte. 2017. A quantum algorithm to train neural networks using low-depth circuits. arXiv preprint arXiv:1712.05304 (2017)."},{"key":"e_1_2_1_258_1","volume-title":"A universal training algorithm for quantum deep learning. arXiv preprint arXiv:1806.09729","author":"Verdon Guillaume","year":"2018","unstructured":"Guillaume Verdon, Jason Pye, and Michael Broughton. 2018. A universal training algorithm for quantum deep learning. arXiv preprint arXiv:1806.09729 (2018)."},{"key":"e_1_2_1_259_1","doi-asserted-by":"crossref","unstructured":"Jeffrey S. Vetter Ron Brightwell Maya Gokhale Pat McCormick Rob Ross John Shalf Katie Antypas David Donofrio Travis Humble Catherine Schuman et al. 2019. Extreme Heterogeneity 2018-Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity. Technical Report. Department of Energy Office of Science.","DOI":"10.2172\/1473756"},{"key":"e_1_2_1_260_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2011.83"},{"key":"e_1_2_1_261_1","volume-title":"Proceedings of the ACM Workshop on High Performance Graph Processing. ACM, 1--8.","author":"Wang Leyuan","unstructured":"Leyuan Wang, Yangzihao Wang, Carl Yang, and John D. Owens. 2016. A comparative study on exact triangle counting algorithms on the GPU. In Proceedings of the ACM Workshop on High Performance Graph Processing. ACM, 1--8."},{"key":"e_1_2_1_262_1","doi-asserted-by":"publisher","DOI":"10.1103\/physreva.88.063853"},{"key":"e_1_2_1_263_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.94.022309"},{"key":"e_1_2_1_264_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-017-1569-z"},{"key":"e_1_2_1_265_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.99.052306"},{"key":"e_1_2_1_266_1","volume-title":"Alibaba cloud quantum development kit: Large-scale classical simulation of quantum circuits. arXiv e-prints arXiv:1907.11217 (July","author":"Zhang Fang","year":"2019","unstructured":"Fang Zhang, Cupjin Huang, Michael Newman, Junjie Cai, Huanjun Yu, Zhengxiong Tian, Bo Yuan, Haihong Xu, Junyin Wu, Xun Gao, Jianxin Chen, Mario Szegedy, and Yaoyun Shi. 2019. Alibaba cloud quantum development kit: Large-scale classical simulation of quantum circuits. arXiv e-prints arXiv:1907.11217 (July 2019)."},{"key":"e_1_2_1_267_1","first-page":"11168","article-title":"Experimental quantum annealing: Case study involving the graph isomorphism problem. Sci","volume":"5","author":"Zick Kenneth M.","year":"2015","unstructured":"Kenneth M. Zick, Omar Shehab, and Matthew French. 2015. Experimental quantum annealing: Case study involving the graph isomorphism problem. Sci. Rep. 5 (2015), 11168.","journal-title":"Rep."}],"container-title":["ACM Transactions on Parallel Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3380940","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3380940","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3380940","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:08:44Z","timestamp":1755882524000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3380940"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,29]]},"references-count":267,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,3,31]]}},"alternative-id":["10.1145\/3380940"],"URL":"https:\/\/doi.org\/10.1145\/3380940","relation":{},"ISSN":["2329-4949","2329-4957"],"issn-type":[{"value":"2329-4949","type":"print"},{"value":"2329-4957","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,29]]},"assertion":[{"value":"2018-11-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-01-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-03-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}