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.
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J.B. acknowledges financial support from AFOSR grant FA9550-16-1-0300, Models and Protocols for Quantum Distributed Computation. P.W. acknowledges financial support from the ERC (Consolidator Grant QITBOX), Spanish Ministry of Economy and Competitiveness (Severo Ochoa Programme for Centres of Excellence in R&D SEV-2015-0522 and QIBEQI FIS2016-80773-P), Generalitat de Catalunya (CERCA Programme and SGR 875), and Fundacio Privada Cellex. P.R. and S.L. acknowledge funding from ARO and AFOSR under MURI programmes. We thank L. Zheglova for producing Fig. 1.
The authors declare no competing financial interests.
Reviewer Information Nature thanks L. Lamata and the other anonymous reviewer(s) for their contribution to the peer review of this work.
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Biamonte, J., Wittek, P., Pancotti, N. et al. Quantum machine learning. Nature 549, 195–202 (2017). https://doi.org/10.1038/nature23474
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