A NASA perspective on quantum computing: Opportunities and challenges

被引:57
作者
Biswas, Rupak [1 ]
Jiang, Zhang [1 ]
Kechezhi, Kostya [1 ]
Knysh, Sergey [1 ]
Mandra, Salvatore [1 ]
O'Gorman, Bryan [1 ]
Perdomo-Ortiz, Alejandro [1 ]
Petukhov, Andre [1 ]
Realpe-Gomez, John [1 ]
Rieffel, Eleanor [1 ]
Venturelli, Davide [1 ]
Vasko, Fedir [1 ]
Wang, Zhihui [1 ]
机构
[1] NASA Ames Res Ctr, Moffett Field, CA 94035 USA
关键词
Quantum computing; Quantum annealing; Combinatorial optimization; Planning and scheduling; Fault diagnosis; Machine learning; Boltzmann sampling; SUPERCONDUCTING CIRCUITS; STATES;
D O I
10.1016/j.parco.2016.11.002
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In the last couple of decades, the world has seen several stunning instances of quantum algorithms that provably outperform the best classical algorithms. For most problems, however, it is currently unknown whether quantum algorithms can provide an advantage, and if so by how much, or how to design quantum algorithms that realize such advantages. Many of the most challenging computational problems arising in the practical world are tackled today by heuristic algorithms that have not been mathematically proven to outperform other approaches but have been shown to be effective empirically. While quantum heuristic algorithms have been proposed, empirical testing becomes possible only as quantum computation hardware is built. The next few years will be exciting as empirical testing of quantum heuristic algorithms becomes more and more feasible. While large-scale universal quantum computers are likely decades away, special-purpose quantum computational hardware has begun to emerge, which will become more powerful over time, as well as small-scale universal quantum computers. (C) 2016 Published by Elsevier B.V.
引用
收藏
页码:81 / 98
页数:18
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