Semantic Scholar Open Access 2016 2550 sitasi

Quantum machine learning

J. Biamonte P. Wittek Nicola Pancotti P. Rebentrost N. Wiebe +1 lainnya

Abstrak

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so 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 quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Topik & Kata Kunci

Physics Mathematics Computer Science Medicine

Penulis (6)

J. Biamonte

P. Wittek

Nicola Pancotti

P. Rebentrost

N. Wiebe

S. Lloyd

Format Sitasi

APA MLA BibTeX

Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S. (2016). Quantum machine learning. https://doi.org/10.1038/nature23474

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Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 2550×
Sumber Database: Semantic Scholar
DOI: 10.1038/nature23474
Akses: Open Access ✓