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We present the application of Restricted Boltzmann Machines (RBMs) to the task of astronomical image classification using a quantum annealer built by D-Wave Systems.
P. H. Barchi, R. R. de Carvalho, R. R. Rosa, R. Sautter, M. Soares-Santos, B. A. D. Marques, E. Clua, T. S. Gonçalves, C. de Sá-Freitas, and T. C. Moura, Machine and Deep Learning Applied to Galaxy Morphology – A Comparative Study , arXiv e-print (Jan 2019) · 1901
Earlier work this paper cites.
M. Ntampaka, C. Avestruz, S. Boada, J. Caldeira, J. Cisewski-Kehe, R. Di Stefano, C. Dvorkin, A. E. Evrard, A. Farahi, D. Finkbeiner et al., The Role of Machine Learning in the Next Decade of Cosmology , arXiv e-print (2019) · 1902
Earlier work this paper cites.
doi:10.1007/BF02650179
R. P. Feynman, Simulating physics with computers , International Journal of Theoretical Physics 21 (6) (1982) 467–488 · 1982
Earlier work this paper cites.
P. Smolensky, Information processing in dynamical systems: Foundations of harmony theory, in: D. E. Rumelhart and J. L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, MA, USA, 1986, pp. 194–281
1986
Earlier work this paper cites.
doi:10.1162/089976602760128018
G. E. Hinton, Training products of experts by minimizing contrastive divergence , Neural Computation 14 (8) (2002) 1771–1800 · 2002
Earlier work this paper cites.
doi:10.1145/1390156.1390224
H. Larochelle and Y. Bengio, Classification using discriminative restricted Boltzmann machines , in: Proceedings of the 25th International Conference on Machine Learning, ICML ’08, ACM, New York, NY, USA, 2008, pp. 536–543 · 2008
Earlier work this paper cites.
V. Choi, Minor-embedding in adiabatic quantum computation: I. the parameter setting problem, Quantum Information Processing 7 (2008) 193–209
2008
Earlier work this paper cites.
doi:10.1038/nature10012
M. Johnson, M. Amin, S. Gildert, T. Lanting, F. Hamze, N. Dickson, R. Harris, A. Berkley, J. Johansson, P. Bunyk et al., Quantum annealing with manufactured spins, Nature 473 (2011) 194–8 · 2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
doi:10.1093/mnras/stt1458
K. W. Willett, C. J. Lintott, S. P. Bamford, K. L. Masters, B. D. Simmons, K. R. V. Casteels, E. M. Edmondson, L. F. Fortson, S. Kaviraj, W. C. Keel et al., Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey, Monthly Notices of the Royal Astronomical Society 435 (4) (2013) 2835–2860 · 2013
Cited alongside, same era.
doi:10.1146/annurev-astro-081913-040037
C. J. Conselice, The evolution of galaxy structure over cosmic time , Annual Review of Astronomy and Astrophysics 52 (1) (2014) 291–337 · 2014
Cited alongside, same era.
doi:10.1051/0004-6361/201425174
Vika, Marina, Vulcani, Benedetta, Bamford, Steven P., Häußler, Boris, and Rojas, Alex L., MegaMorph: classifying galaxy morphology using multi-wavelength Sérsic profile fits , A&A 577 (2015) A97 · 2015
Cited alongside, same era.
S. Dieleman, K. W. Willett, and J. Dambre, Rotation-invariant convolutional neural networks for galaxy morphology prediction, Monthly Notices of the Royal Astronomical Society 450 (2) (2015) 1441–1459 · 2015
Cited alongside, same era.
A. Mott, J. Job, J.-R. Vlimant, D. Lidar, and M. Spiropulu, Solving a higgs optimization problem with quantum annealing for machine learning , Nature 550 (2017) 375 EP –. URL https://doi.org/10.1038/nature24047
2017
Later among the works it cites.
D. Tuccillo, M. Huertas-Company, E. Decencière, and S. Velasco-Forero, Deep learning for studies of galaxy morphology, in: M. Brescia, S. G. Djorgovski, E. D. Feigelson, G. Longo, and S. Cavuoti (Eds.), Astroinformatics, Vol. 325 of IAU Symposium, 2017, pp. 191–196 · 2017
Later among the works it cites.
arXiv:1807.02876[physics.comp-ph]
K. Albertsson, P. Altoe, D. Anderson, J. Anderson, M. Andrews, J. P. Araque Espinosa, A. Aurisano, L. Basara, A. Bevan, W. Bhimji et al., Machine Learning in High Energy Physics Community White Paper , arXiv e-print (2018) · 2018
Later among the works it cites.
A. Vahdat, E. Andriyash, and W. G. Macready, DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors , arXiv e-print (2018) · 2018
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S. H. Adachi and M. P. Henderson, Application of quantum annealing to training of deep neural networks , arXiv e-print (2015) · 2015
Cited alongside, same era.
doi:10.1103/PhysRevA.92.052323
M. H. Amin, Searching for quantum speedup in quasistatic quantum annealers , Phys. Rev. A 92 (2015) 052323 · 2015
Cited alongside, same era.
doi:10.1103/PhysRevA.94.022308
M. Benedetti, J. Realpe-Gómez, R. Biswas, and A. Perdomo-Ortiz, Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning , Phys. Rev. A 94 (2016) 022308 · 2016
Cited alongside, same era.
J. T. Rolfe, Discrete Variational Autoencoders , arXiv e-print (2016) · 2016
Cited alongside, same era.
doi:10.1126/science.aah4243
T. Inagaki, Y. Haribara, K. Igarashi, T. Sonobe, S. Tamate, T. Honjo, A. Marandi, P. L. McMahon, T. Umeki, K. Enbutsu et al., A coherent ising machine for 2000-node optimization problems , Science 354 (6312) (2016) 603–606 · 2016
Cited alongside, same era.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning , Nature 549 (2017) 195 EP –. URL https://doi.org/10.1038/nature23474
2017
Cited alongside, same era.
Later among the works it cites.
Large Synoptic Survey Telescope, https://www.lsst.org/ (2019)
2019
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D-Wave Systems, The D-Wave 2000Q™system, https://www.dwavesys.com/d-wave-two-system (2019)
2019
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C. McGeoch, Comparison of 2000Q to Lower-Noise 2000Q , Qubits North America 2019 (2019). URL https://www.dwavesys.com/sites/default/files/14-DWMcGeogh.pdf
2019
Closest in time.
doi:10.3389/fphy.2019.00048
M. Aramon, G. Rosenberg, E. Valiante, T. Miyazawa, H. Tamura, and H. G. Katzgraber, Physics-inspired optimization for quadratic unconstrained problems using a digital annealer , Frontiers in Physics 7 (2019) 48 · 2019
Closest in time.
doi:10.1007/s11128-012-0506-4
K. L. Pudenz and D. A. Lidar, Quantum adiabatic machine learning , Quantum Information Processing 12 (5) (2013) 2027–2070 · 2070
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