On quantum methods for machine learning problems part II: Quantum classification algorithms Article Swipe

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Farid Ablayev , Marat Ablayev , Joshua Zhexue Huang , Kamil Khadiev , Nailya Salikhova , Dingming Wu ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.26599/bdma.2019.9020018

This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines. We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification.

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Quantum Machine Learning

Artificial Intelligence

Physics

Quantum Mechanics

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Quantum machine learning Quantum Computer science Quantum algorithm Algorithm Machine learning Artificial intelligence Physics Quantum mechanics

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Type: article
Language: en
Landing Page: https://doi.org/10.26599/bdma.2019.9020018
PDF: https://ieeexplore.ieee.org/ielx7/8254253/8935088/08935095.pdf
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Cited By: 32
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DOI: https://doi.org/10.26599/bdma.2019.9020018

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Title: On quantum methods for machine learning problems part II: Quantum classification algorithms

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Language: en

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Publication year: 2019

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Publication date: 2019-12-19

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Authors: Farid Ablayev, Marat Ablayev, Joshua Zhexue Huang, Kamil Khadiev, Nailya Salikhova, Dingming Wu

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Landing page: https://doi.org/10.26599/bdma.2019.9020018

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Concepts: Quantum machine learning, Quantum, Computer science, Quantum algorithm, Algorithm, Machine learning, Artificial intelligence, Physics, Quantum mechanics

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Cited by: 32

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institutions_distinct_count	6
citation_normalized_percentile.value	0.88058849
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False