当前位置: X-MOL 学术Nature › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Quantum machine learning
Nature ( IF 64.8 ) Pub Date : 2017-09-01 , DOI: 10.1038/nature23474
Jacob Biamonte , Peter Wittek , Nicola Pancotti , Patrick Rebentrost , Nathan Wiebe , Seth Lloyd

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.

中文翻译:

量子机器学习

在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强大工具。量子系统会产生经典系统被认为无法有效产生的非典型模式,因此假设量子计算机在机器学习任务上可能优于经典计算机是合理的。量子机器学习领域探索如何设计和实现量子软件,使机器学习比经典计算机更快。最近的工作产生了可以作为机器学习程序构建块的量子算法,但硬件和软件挑战仍然相当大。
更新日期:2017-09-01
down
wechat
bug