Quantum machine learning
Top Cited Papers
- 14 September 2017
- journal article
- review article
- Published by Springer Science and Business Media LLC in Nature
- Vol. 549 (7671), 195-202
- https://doi.org/10.1038/nature23474
Abstract
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.Keywords
This publication has 71 references indexed in Scilit:
- Parallel photonic information processing at gigabyte per second data rates using transient statesNature Communications, 2013
- Quantum learning without quantum memoryScientific Reports, 2012
- A quantum–quantum Metropolis algorithmProceedings of the National Academy of Sciences of the United States of America, 2012
- Quantum learning algorithms for quantum measurementsPhysics Letters A, 2011
- Quantum Algorithm for Linear Systems of EquationsPhysical Review Letters, 2009
- Quantum pattern recognition with liquid-state nuclear magnetic resonancePhysical Review A, 2009
- Realizable Hamiltonians for universal adiabatic quantum computersPhysical Review A, 2008
- Quantum Random Access MemoryPhysical Review Letters, 2008
- Quantum optimization for training support vector machinesNeural Networks, 2003
- The perceptron: A probabilistic model for information storage and organization in the brain.Psychological Review, 1958