Circuit-centric quantum classifiers

A machine learning design is developed to train a quantum circuit specialized in solving a classification problem. In addition to discussing the training method and effect of noise, it is shown that the circuits perform reasonably well on classical benchmarks.

Quantum Information, Science & Technology
Machine learning
Quantum algorithms
Quantum computation
machine learning
noise
benchmark
quantum classifier
quantum algorithm
quantum circuit

References(28)

Figures(3)

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M.A. Nielsen
,
I.L. Chuang

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John Preskill
(
- Caltech, IQI
)

- Quantum 2 (2018) 79
•
e-Print:
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•
DOI:
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Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

Alejandro Perdomo-Ortiz
(
- NASA, Ames and
- Unlisted, US and
- University Coll. London and
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Marcello Benedetti
(
- NASA, Ames and
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John Realpe-Gómez
(
- NASA, Ames and
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Rupak Biswas
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- NASA, Ames
)

- Quantum Sci.Technol. 3 (2018) 3, 030502
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M. Schuld
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Jacob Biamonte
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- Skoltech and
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Peter Wittek
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- ICFO, Barcelona
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Nicola Pancotti
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- Munich, Max Planck Inst. Quantenopt.
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Patrick Rebentrost
(
- MIT
)
,
Nathan Wiebe
(
- Microsoft, Redmond
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et al.

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Edward Farhi
(
- MIT, Cambridge, CTP
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,
Jeffrey Goldstone
(
- MIT, Cambridge, CTP
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Sam Gutmann
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- MIT, Cambridge, CTP
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M.J. Bremner
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Joonsuk Huh
(
- Harvard U. (main)
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Gian Giacomo Guerreschi
(
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Borja Peropadre
(
- Harvard U. (main)
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Jarrod R. McClean
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- Harvard U. (main)
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Alán Aspuru-Guzik
(
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)

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The theory of variational hybrid quantum-classical algorithms

Jarrod R. McClean
(
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Jonathan Romero
(
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Ryan Babbush
(
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Alán Aspuru-Guzik
(
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Quantum circuit learning

Kosuke Mitarai
(
- Osaka U.
)
,
Makoto Negoro
(
- Osaka U. and
- ERATO, JST, Tokyo
)
,
Masahiro Kitagawa
(
- Osaka U.
)
,
Keisuke Fujii
(
- ERATO, JST, Tokyo and
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)

- Phys.Rev.A 98 (2018) 3, 032309,
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A quantum algorithm to train neural networks using low-depth circuits

e-Print:
- 1712.05304

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design, Deep learning and quantum entanglement: Fundamental connections with implications to network design