Information-Theoretic Bounds on Quantum Advantage in Machine Learning

Hsin-Yuan Huang; John Preskill; Richard Küng

doi:10.1103/PhysRevLett.126.190505

Information-Theoretic Bounds on Quantum Advantage in Machine Learning

Hsin-Yuan Huang
, John Preskill
, Richard Küng

Department of Quantum Computing

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	190505
Pages (from-to)	190505
Journal	Physical Review Letters
Volume	126
Issue number	19
DOIs	https://doi.org/10.1103/PhysRevLett.126.190505
Publication status	Published - 14 May 2021

Fields of science

102 Computer Sciences
202 Electrical Engineering, Electronics, Information Engineering

JKU Focus areas

Digital Transformation

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10.1103/PhysRevLett.126.190505

Cite this

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title = "Information-Theoretic Bounds on Quantum Advantage in Machine Learning",

author = "Hsin-Yuan Huang and John Preskill and Richard K{\"u}ng",

year = "2021",

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journal = "Physical Review Letters",

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T1 - Information-Theoretic Bounds on Quantum Advantage in Machine Learning

AU - Huang, Hsin-Yuan

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AU - Küng, Richard

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Information-Theoretic Bounds on Quantum Advantage in Machine Learning

Fields of science

JKU Focus areas

Access to Document

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