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P. W. Shor, SIAM J. Comput. 26
1997
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H. Jaeger and H. Haas, Science 304
2004
Earlier work this paper cites.
R. Raussendorf, Phys. Rev. A 72
2005
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D. Janzing and P. Wocjan, Quantum Inf. Process. 4
2005
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C. M. Bishop, Pattern Recognition and Machine Learning (Spriger, New York, 2006)
2006
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J. Nocedal and S. Wright, Numerical Optimization (Springer, New York, 2006)
2006
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A. W. Harrow, A. Hassidim, and S. Lloyd, Phys. Rev. Lett. 103
2009
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M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information (Cambridge University Press, Cambridge, 2010)
2010
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M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld, Phys. Rev. Lett. 108
2012
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N. Wiebe, D. Braun, and S. Lloyd, Phys. Rev. Lett. 109
2012
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2018

Mitarai, Kosuke, Negoro, Makoto, Kitagawa, Masahiro et al.

We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning.

A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it.
The iterative optimization of the parameters allows us to circumvent the high-depth circuit.
Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations.

P. W. Shor, SIAM J. Comput. 26
1997
Earlier work this paper cites.
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2004
Earlier work this paper cites.
R. Raussendorf, Phys. Rev. A 72
2005
Earlier work this paper cites.
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Earlier work this paper cites.
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