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2024

Classification of the Fashion-MNIST Dataset on a Quantum Computer

Shen, Kevin, Jobst, Bernhard, Shishenina, Elvira et al.

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The potential impact of quantum machine learning algorithms on industrial applications remains an exciting open question.

Conventional methods for encoding classical data into quantum computers are not only too costly for a potential quantum advantage in the algorithms but also severely limit the scale of feasible experiments on current hardware.
Therefore, recent works, despite claiming the near-term suitability of their algorithms, do not provide experimental benchmarking on standard machine learning datasets.
We attempt to solve the data encoding problem by improving a recently proposed variational algorithm [1] that approximately prepares the encoded data, using asymptotically shallow circuits that fit the native gate set and topology of currently available quantum computers.

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Built on

M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, Quantum Science and Technology 4
Original
1906
Earlier work this paper cites.
S.-J. Ran, Physical Review A 101
Original
1908
Earlier work this paper cites.
A. Smith, B. Jobst, A. G. Green, and F. Pollmann, Physical Review Research 4
Original
1910
Earlier work this paper cites.
R. E. Bechhofer, S. Elmaghraby, and N. Morse, The Annals of Mathematical Statistics 30
1959
Earlier work this paper cites.
C. Burges and C. J. Burges, Data Mining and Knowledge Discovery 2
1998
Earlier work this paper cites.
J. Weston and C. Watkins (1999) pp. 219–224
1999
Earlier work this paper cites.
V. N. Vapnik, The Nature of Statistical Learning Theory (Springer, New York, NY, 2000)
2000
Earlier work this paper cites.
S. Lloyd, M. Schuld, A. Ijaz, J. Izaac, and N. Killoran, Quantum embeddings for machine learning (2020), arXiv:2001.03622 [quant-ph]
Original
2001
Earlier work this paper cites.
B. Kraus and J. I. Cirac, Physical Review A 63
2001
Earlier work this paper cites.
C.-W. Hsu and C.-J. Lin, IEEE Transactions on Neural Networks 13
2002
Earlier work this paper cites.
J. J. Vartiainen, M. Möttönen, and M. M. Salomaa, Physical Review Letters 92
2004
Earlier work this paper cites.
F. Vatan and C. Williams, Physical Review A 69
2004
Earlier work this paper cites.
C. Schön, E. Solano, F. Verstraete, J. I. Cirac, and M. M. Wolf, Physical Review Letters 95
2005
Earlier work this paper cites.
C. Schön, K. Hammerer, M. M. Wolf, J. I. Cirac, and E. Solano, Physical Review A 75
2007
Earlier work this paper cites.
I. A. Luchnikov, M. E. Krechetov, and S. N. Filippov, New Journal of Physics 23
Original
2007
Earlier work this paper cites.
V. Giovannetti, S. Lloyd, and L. Maccone, Physical Review Letters 100
Original
2008
Earlier work this paper cites.
G. Vidal, Physical Review Letters 101
2008
Earlier work this paper cites.
A. W. Harrow, A. Hassidim, and S. Lloyd, Physical Review Letters 103
Original
2009
Earlier work this paper cites.
A. Gilyén, Z. Song, and E. Tang, Quantum 6
Original
2009
Earlier work this paper cites.
A. Oza, A. Pechen, J. Dominy, V. Beltrani, K. Moore, and H. Rabitz, Journal of Physics A: Mathematical and Theoretical 42
Original
2009
Earlier work this paper cites.
H.-Y. Huang, M. Broughton, M. Mohseni, R. Babbush, S. Boixo, H. Neven, and J. R. McClean, Nature Communications 12
Original
2011
Earlier work this paper cites.
M. Plesch and Ä. Brukner, Physical Review A 83
2011
Earlier work this paper cites.
K. Zhang, M.-H. Hsieh, L. Liu, and D. Tao, Toward Trainability of Quantum Neural Networks (2020), arXiv:2011.06258 [quant-ph]
Original
2011
Earlier work this paper cites.
U. Schollwoeck, Annals of Physics 326
Original
2011
Earlier work this paper cites.
P. Q. Le, F. Dong, and K. Hirota, Quantum Information Processing 10
2011
Earlier work this paper cites.

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Later among the works it cites.

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