A quantum algorithm to train neural networks using low-depth circuits

Verdon, Guillaume; Broughton, Michael; Biamonte, Jacob

A quantum algorithm to train neural networks using low-depth circuits

Dec 14, 2017

e-Print:

1712.05304 [quant-ph]

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Abstract: (submitter)

Can near-term gate model based quantum processors offer quantum advantage for practical applications in the pre-fault tolerance noise regime? A class of algorithms which have shown some promise in this regard are the so-called classical-quantum hybrid variational algorithms. Here we develop a low-depth quantum algorithm to generative neural networks using variational quantum circuits. We introduce a method which employs the quantum approximate optimization algorithm as a subroutine in order produce then sample low-energy distributions of Ising Hamiltonians. We sample these states to train neural networks and demonstrate training convergence for numerically simulated noisy circuits with depolarizing errors of rates of up to

4\%

.

quantum circuit: variational
neural network
quantum algorithm
noise
Hamiltonian
hybrid
quantum advantage
quantum approximate optimization algorithm
gate

References(20)

Figures(4)

[1]

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Jacob Biamonte
(
- Skoltech and
- Waterloo U.
)
,
Peter Wittek
(
- ICFO, Barcelona
)
,
Nicola Pancotti
(
- Munich, Max Planck Inst. Quantenopt.
)
,
Patrick Rebentrost
(
- MIT
)
,
Nathan Wiebe
(
- Microsoft, Redmond
)

et al.

- Nature 549 (2017) 7671, 195-202
•
e-Print:
- 1611.09347
•
DOI:
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[2]

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Edward Farhi
(
- MIT, Cambridge, CTP
)
,
Jeffrey Goldstone
(
- MIT, Cambridge, CTP
)
,
Sam Gutmann
(
- MIT, Cambridge, CTP
)

e-Print:
- 1411.4028

[3]

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Jarrod R. McClean
(
- LBNL, Berkeley
)
,
Jonathan Romero
(
- Harvard U. (main)
)
,
Ryan Babbush
(
- Unlisted, US, CA
)
,
Alán Aspuru-Guzik
(
- Harvard U. (main)
)

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•
e-Print:
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•
DOI:
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[4]

From the quantum approximate optimization algorithm to a quantum alternating operator ansatz.

Stuart Hadfield
(
- NASA, Ames and
- Google Inc. and
- Columbia U. and
- Columbia U., Astron. Astrophys.
)
,
Zhihui Wang
(
- NASA, Ames and
- Google Inc.
)
,
Bryan O'Gorman
(
- NASA, Ames and
- UC, Berkeley (main)
)
,
Eleanor G. Rieffel
(
- NASA, Ames
)
,
Davide Venturelli
(
- NASA, Ames and
- Google Inc.
)

et al.

- Algorithms (Basel) 12 (2019) 2, 34,
- Algorithms 12.2 (2019): 34. (Special issue "Quantum Optimization Theory, Algorithms, and Applications")
•
e-Print:
- 1709.03489
•
DOI:
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