Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology, Skoltech Building 3, Moscow 143026, Russia.
Institute for Quantum Computing, University of Waterloo, Waterloo, N2L 3G1 Ontario, Canada.
Nature. 2017 Sep 13;549(7671):195-202. doi: 10.1038/nature23474.
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.
在不断增加的计算机能力和算法进步的推动下，机器学习技术已成为在数据中寻找模式的有力工具。量子系统产生的模式是非典型的，经典系统被认为不能有效地产生这些模式，因此可以合理地假设量子计算机在机器学习任务上可能优于经典计算机。量子机器学习领域探索如何设计和实现量子软件，以实现比经典计算机更快的机器学习。最近的工作已经产生了可以作为机器学习程序构建块的量子算法，但硬件和软件方面的挑战仍然相当大。
