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Quantum Machine Learning

2016/11/28 by Jacob Biamonte, Peter Wittek, Péter Wittek +4 · 3 voices · 4,578 citations
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Computer engineering #Computer science #Field (mathematics) #Mathematics #Neural Networks and Reservoir Computing #Physics #Programming language #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum computer #Quantum machine learning #Software #cond-mat.str-el #quant-ph #stat.ML

paper · pdf · doi:10.1038/nature23474

published in Nature 549(7671), 195-202 (Nature Portfolio) · 24 pages, 2 figures

openalex publication_date 2017/09/01 · arxiv created 2018/05/10 · arxiv updated 2018/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement concrete quantum software that offers such advantages. Recent work has made clear that the hardware and software challenges are still considerable but has also opened paths towards solutions.

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