Quantum-enhanced data classification with a variational entangled sensor network

Yi Xia; Wei Li; Quntao Zhuang; Quntao Zhuang; Zheshen Zhang; Zheshen Zhang

doi:10.1364/CLEO_QELS.2021.FW3N.2

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper FW3N.2
•https://doi.org/10.1364/CLEO_QELS.2021.FW3N.2

Quantum-enhanced data classification with a variational entangled sensor network

Yi Xia, Wei Li, Quntao Zhuang, and Zheshen Zhang

CLEO: QELS_Fundamental Science 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Machine Learning and Applications (FW3N)

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Y. Xia, W. Li, Q. Zhuang, and Z. Zhang, "Quantum-enhanced data classification with a variational entangled sensor network," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper FW3N.2.

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Abstract

We report the experimental demonstration of supervised learning assisted by an entangled sensor network (SLAEN). We show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals.

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