Quantum-Assisted Cluster Analysis on a Quantum Annealing Device
We present an algorithm for quantum-assisted cluster analysis that makes use of the topological properties of a D-Wave 2000Q quantum processing unit. Clustering is a form of unsupervised machine learning, where instances are organized into groups whose members share similarities. The assignments are...
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| Vydané v: | Frontiers in physics Ročník 6 |
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| Hlavní autori: | , , |
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| Jazyk: | English |
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Frontiers Media S.A
14.06.2018
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| Abstract | We present an algorithm for quantum-assisted cluster analysis that makes use of the topological properties of a D-Wave 2000Q quantum processing unit. Clustering is a form of unsupervised machine learning, where instances are organized into groups whose members share similarities. The assignments are, in contrast to classification, not known a priori, but generated by the algorithm. We explain how the problem can be expressed as a quadratic unconstrained binary optimization problem and show that the introduced quantum-assisted clustering algorithm is, regarding accuracy, equivalent to commonly used classical clustering algorithms. Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems [1], have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization, sampling, and clustering [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical clustering algorithm. |
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| AbstractList | We present an algorithm for quantum-assisted cluster analysis that makes use of the topological properties of a D-Wave 2000Q quantum processing unit. Clustering is a form of unsupervised machine learning, where instances are organized into groups whose members share similarities. The assignments are, in contrast to classification, not known a priori, but generated by the algorithm. We explain how the problem can be expressed as a quadratic unconstrained binary optimization problem and show that the introduced quantum-assisted clustering algorithm is, regarding accuracy, equivalent to commonly used classical clustering algorithms. Quantum annealing algorithms belong to the class of metaheuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum annealing machines produced by D-Wave Systems [1], have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization, sampling, and clustering [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Our first and foremost aim is to explain how to represent and solve parts of these problems with the help of the QPU, and not to prove supremacy over every existing classical clustering algorithm. |
| Author | Neukart, Florian Dollen, David Von Seidel, Christian |
| Author_xml | – sequence: 1 givenname: Florian surname: Neukart fullname: Neukart, Florian – sequence: 2 givenname: David Von surname: Dollen fullname: Dollen, David Von – sequence: 3 givenname: Christian surname: Seidel fullname: Seidel, Christian |
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| Cites_doi | 10.1038/nphys2900 10.1140/epjst/e2015-02347-y 10.1007/978-3-658-16176-7_3 10.1103/PhysRevLett.117.180402 10.1007/s11128-014-0892-x 10.3389/fict.2017.00029 10.1007/3-540-33752-0 10.3389/fphy.2014.00005 10.1103/PhysRevLett.118.066802 10.1103/PhysRevX.5.031040 10.1007/978-3-658-16176-7_8 10.3389/fphy.2014.00052 10.1016/0009-2614(94)00117-0 10.1103/PhysRevX.4.021041 10.1007/s11128-017-1527-9 10.1140/epjst/e2015-02349-9 10.3389/fphy.2017.00071 10.1038/srep00571 10.1007/978-3-540-79739-5 10.1103/PhysRevA.94.022308 10.1016/j.proeng.2014.03.148 10.1145/3084688 |
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| References | Neukart (B15) 2018; 5 Finilla (B29) 1994; 219 Von Dollen (B17) 2017 Neukart (B25) 2013; 2 Lanting (B20) 2013; 4 Neukart (B28) 2017; 4 Anderberg (B31) 1973 Neukart (B23) 2014; 69 MacQueen (B33) 1967 Perdomo-Ortiz (B10) 2014; 224 Neukart (B19) 2017 Smolin (B13) 2013; 2 Jiang (B5) 2015; 16 B34 Kumar (B21) 2018 Pedregosa (B36) 2011; 12 Rieffel (B8) 2014; 14 Korenkevych (B24) 2016 B16 Smelyanskiy (B3) 2015; 118 Venturelli (B9) 2014 B18 Isakov (B6) 2015; 117 Babbush (B12) 2012 Kramer (B37) 2009 Ritter (B35) 1991 B1 Venturelli (B4) 2015 Benedetti (B2) 2015; 94 Chamoni (B32) 2006 Levit (B26) 2017 Lucas (B22) 2014; 2 O'Gorman (B7) 2015; 224 Neukart (B30) 2017 Perdomo-Ortiz (B14) 2012; 2 Crawford (B27) 2016 Boixo (B11) 2014; 10 |
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