A Leap among Quantum Computing and Quantum Neural Networks: A Survey

被引:20
作者
Massoli, Fabio Valerio [1 ]
Vadicamo, Lucia [1 ]
Amato, Giuseppe [1 ]
Falchi, Fabrizio [1 ]
机构
[1] CNR, Ist Sci & Tecnol Informaz Alessandro Faedo, Via G Moruzzi 1, I-56124 Pisa, Italy
基金
欧盟地平线“2020”;
关键词
Quantum computing; quantum machine learning; quantum neural network; quantum deep learning; COMPUTATIONAL-COMPLEXITY; DISCRETE LOGARITHMS; LEARNING ALGORITHM; OPTIMIZATION; SUPREMACY;
D O I
10.1145/3529756
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community's interest since the late '80s. In such a context, we propose our contribution. First, we introduce basic concepts related to quantum computations, and then we explain the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms. Finally, we gather, compare, and analyze the current state-of-the-art concerning Quantum Perceptrons and Quantum Neural Networks implementations.
引用
收藏
页数:37
相关论文
共 199 条
[21]   Training deep quantum neural networks [J].
Beer, Kerstin ;
Bondarenko, Dmytro ;
Farrelly, Terry ;
Osborne, Tobias J. ;
Salzmann, Robert ;
Scheiermann, Daniel ;
Wolf, Ramona .
NATURE COMMUNICATIONS, 2020, 11 (01)
[22]  
Behrman E. C., 1996, P 4 WORKSH PHYS COMP, P22
[23]   Parameterized quantum circuits as machine learning models [J].
Benedetti, Marcello ;
Lloyd, Erika ;
Sack, Stefan ;
Fiorentini, Mattia .
QUANTUM SCIENCE AND TECHNOLOGY, 2019, 4 (04)
[24]   THE COMPUTER AS A PHYSICAL SYSTEM - A MICROSCOPIC QUANTUM-MECHANICAL HAMILTONIAN MODEL OF COMPUTERS AS REPRESENTED BY TURING-MACHINES [J].
BENIOFF, P .
JOURNAL OF STATISTICAL PHYSICS, 1980, 22 (05) :563-591
[25]   QUANTUM CRYPTOGRAPHY USING ANY 2 NONORTHOGONAL STATES [J].
BENNETT, CH .
PHYSICAL REVIEW LETTERS, 1992, 68 (21) :3121-3124
[26]   Strengths and weaknesses of quantum computing [J].
Bennett, CH ;
Bernstein, E ;
Brassard, G ;
Vazirani, U .
SIAM JOURNAL ON COMPUTING, 1997, 26 (05) :1510-1523
[27]   A scalable readout system for a superconducting adiabatic quantum optimization system [J].
Berkley, A. J. ;
Johnson, M. W. ;
Bunyk, P. ;
Harris, R. ;
Johansson, J. ;
Lanting, T. ;
Ladizinsky, E. ;
Tolkacheva, E. ;
Amin, M. H. S. ;
Rose, G. .
SUPERCONDUCTOR SCIENCE & TECHNOLOGY, 2010, 23 (10)
[28]   Quantum complexity theory [J].
Bernstein, E ;
Vazirani, U .
SIAM JOURNAL ON COMPUTING, 1997, 26 (05) :1411-1473
[29]   Iterative quantum-assisted eigensolver [J].
Bharti, Kishor ;
Haug, Tobias .
PHYSICAL REVIEW A, 2021, 104 (05)
[30]   Machine learning meets quantum foundations: A brief survey [J].
Bharti, Kishor ;
Haug, Tobias ;
Vedral, Vlatko ;
Kwek, Leong-Chuan .
AVS QUANTUM SCIENCE, 2020, 2 (03)