Quantum machine learning

被引:2519
作者
Biamonte, Jacob [1 ,2 ]
Wittek, Peter [3 ]
Pancotti, Nicola [4 ]
Rebentrost, Patrick [5 ]
Wiebe, Nathan [6 ]
Lloyd, Seth [7 ]
机构
[1] Skolkovo Inst Sci & Technol, Quantum Complex Sci Initiat, Skoltech Bldg 3, Moscow 143026, Russia
[2] Univ Waterloo, Inst Quantum Comp, Waterloo, ON N2L 3G1, Canada
[3] ICFO, Barcelona 08860, Spain
[4] Max Planck Inst Quantum Opt, 1 Hans Kopfermannstr, D-85748 Garching, Germany
[5] MIT, Elect Res Lab, Cambridge, MA 02139 USA
[6] Microsoft Res, Stn Q, Quantum Architectures & Computat Grp, Redmond, WA 98052 USA
[7] MIT, Dept Mech Engn, Cambridge, MA 02139 USA
关键词
ALGORITHMS; PERCEPTRON;
D O I
10.1038/nature23474
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:195 / 202
页数:8
相关论文
共 109 条
[1]   Read the fine print [J].
Aaronson, Scott .
NATURE PHYSICS, 2015, 11 (04) :291-293
[2]  
Adachi S. H., 2015, ARXIV151006356
[3]  
Alvarez-Rodriguez U., 2016, QUANTUM MACHINE LEAR
[4]   GENETIC ALGORITHM WITH MIGRATION ON TOPOLOGY CONSERVING MAPS FOR OPTIMAL-CONTROL OF QUANTUM-SYSTEMS [J].
AMSTRUP, B ;
TOTH, GJ ;
SZABO, G ;
RABITZ, H ;
LORINCZ, A .
JOURNAL OF PHYSICAL CHEMISTRY, 1995, 99 (14) :5206-5213
[5]   Quantum optimization for training support vector machines [J].
Anguita, D ;
Ridella, S ;
Rivieccio, F ;
Zunino, R .
NEURAL NETWORKS, 2003, 16 (5-6) :763-770
[6]  
[Anonymous], 2006, ADV ARTIFICIAL INTEL
[7]  
[Anonymous], QUANTUM LINEAR SYSTE
[8]  
[Anonymous], 2016, QUANTUM BOLTZMANN MA
[9]  
[Anonymous], 2014, QUANTUM DEEP LEARNIN
[10]  
[Anonymous], QUANTUM LINEAR SYSTE