Abstract
Reservoir computing is a framework used to exploit natural nonlinear dynamics with many degrees of freedom, which is called a reservoir, for a machine learning task. Here we introduce the NMR implementation of quantum reservoir computing and quantum extreme learning machine using the nuclear quantum reservoir. The implementation utilizes globally controlled dynamics of nuclear spin qubits in solid state and it has been demonstrated.
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References
A. Abragam, M. Goldman, Nuclear Magnetism: Order and Disorder (Clarendon Press, 1982)
G.A. Alvalez, D. Suter, R. Kaiser, Science 349, 846 (2015)
F. Arute et al., Nature 574, 505 (2019).
S.C. Benjamin, S. Bose, Phys. Rev. Lett. 90, 247901 (2003)
H. Bernien et al., Nature 551, 579–584 (2017)
J. Biamonte et al., Nature 549, 195–202 (2017)
S.L. Braunstein et al., Phys. Rev. Lett. 83, 1054–1057 (1998)
J.B. Butcher, D. Verstraeten, B. Schrauwen, C.R. Day, P.W. Haycock, Neural Netw. 38, 76 (2013)
H. Cho, J. Baugh, C.A. Ryan, D.G. Cory, C. Ramanathan, J. Magn. Reson. 187, 242 (2007)
D.G. Cory, A.F. Fahmy, T.F. Havel, Proc. Nat. Acad. Sci. U. S. A. 94, 1634 (1997)
W. de Boer, T.O. Niinikoski, Nucl. Instrum. Methods 114, 495 (1974)
D.P. DiVincenzo, Fortsch. Phys. 48, 771–783 (2000)
K. Fujii, K. Nakajima, Phys. Rev. Appl. 8, 024030 (2017)
K. Fujii, M. Negoro, N. Imoto, M. Kitagawa, Phys. Rev. X 4, 041039 (2014)
N.A. Gershenfeld, I.L. Chuang, Science 275, 350 (1997)
V. Halvicek et al., Nature 567, 209–212 (2019)
H. Jaeger, H. Haas, Science 304, 78 (2004)
N.C. Jones, R. Van Meter, A.G. Fowler, P.L. McMahon, J. Kim, T.D. Ladd, Y. Yamamoto, Phys. Rev. X 2, 031007 (2012)
E. Knill, R. Laflamme, Phys. Rev. Lett. 81, 5672 (1998)
T.D. Ladd, D. Maryenko, Y. Yamamoto, E. Abe, K.M. Itoh, Phys. Rev. B 71, 014401 (2005)
T.D. Ladd et al., Nature 464, 45–53 (2010)
Z. Li, X. Liu, N. Xu, J. Du, Phys. Rev. Lett. 114, 140504 (2015)
S. Lloyd, Science 261, 1569–1571 (1993)
W. Maass, T. Natschlager, H. Markram, Neural Comput. 14, 2531 (2002)
A. Mazurenko et al., Nature 545, 462–466 (2017)
T. Morimae, K. Fujii, J.F. Fitzsimons, Phys. Rev. Lett. 112, 130502 (2014)
K. Nakajima et al., Sci. Rep. 5, 10487 (2015)
K. Nakajima, K. Fujii, M. Negoro, K. Mitarai, M. Kitagawa, Phys. Rev. Appl. 11, 034021 (2019)
M. Negoro, K. Mitarai, K. Fujii, K. Nakajima, M. Kitagawa (2018), https://arxiv.org/abs/1806.10910
C. Negrevergne et al., Phys. Rev. A 71, 032344 (2005)
M.A. Nielsen, I.L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, 2000)
J. Preskill (2012), https://arxiv.org/abs/1203.5813
C.A. Ryan, O. Moussa, J. Baugh, R. Laflamme, Phys. Rev. Lett. 100, 140501Â (2008)
C.P. Slichter, Principles of Magnetic Resonance, Third Enlarged and Updated edn. (Springer, Berlin, 1990)
K. Tateishi et al., Proc. Natl. Acad. Sci. U. S. A. 111, 7527 (2014)
J. Torrejon et al., Nature 547, 428 (2017)
L.M.K. Vandersypen et al., Nature 414, 883–887 (2001)
K. Vandoorne et al., Nat. Commun. 4, 1364 (2013)
D. Varsraeten, B. Schrauwen, M.D. Haene, D. Stroobandt, Neural Netw. 20, 391 (2007). 5
C.M. Wilson et al. (2018), https://arxiv.org/abs/1806.08321
J. Zhang et al., Nature 551, 601–604 (2017)
Acknowledgements
M.N. is supported by JST PRESTO Grant Number JPMJPR15E7. K.M. is supported by JSPS KAKENHI No. 19J10978. We are supported by MEXT Quantum Leap Flagship Program (MEXT Q-LEAP) Grant Number JPMXS0118067394. We acknowledge Masahiro Kitagawa.
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Negoro, M., Mitarai, K., Nakajima, K., Fujii, K. (2021). Toward NMR Quantum Reservoir Computing. In: Nakajima, K., Fischer, I. (eds) Reservoir Computing. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-13-1687-6_19
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DOI: https://doi.org/10.1007/978-981-13-1687-6_19
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