量子退火与机器学习Quantum Annealing meets Machine Learning |
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课程网址: | http://videolectures.net/uai2012_macready_quantum_annealing/ |
主讲教师: | William Macready |
开课单位: | D波系统公司 |
开课时间: | 2012-09-17 |
课程语种: | 英语 |
中文简介: | 量子计算提供了理论上的承诺,即通过直接利用现实的潜在量子方面来显着加快计算速度。这个想法于20世纪80年代初首次提出,随着Peter Shor发现了多项式时间整数因子分解算法而引起了人们的兴趣。今天,实现小规模量子算法的第一个实验平台正变得司空见惯。有趣的是,机器学习可能是“杀手级应用程序”。用于量子计算。我们将介绍量子算法,重点关注最近的量子计算模型,这对于具有图形模型背景的研究人员来说是熟悉的。我们将展示在当前量子硬件上运行的特定量子算法 - 量子退火 - 如何应用于机器学习中出现的某些优化问题。反过来,我们将描述量子计算进一步发展的一些挑战,并建议机器学习研究人员可以很好地推动量子退火的第一个真实应用。 |
课程简介: | Quantum Computing offers the theoretical promise of dramatically faster computation through direct utilization of the underlying quantum aspects of reality. This idea, first proposed in the early 1980s, exploded in interest in 1994 with Peter Shor's discovery of a polynomial time integer factoring algorithm. Today the first experimental platforms realizing small-scale quantum algorithms are becoming commonplace. Interestingly, machine learning may be the "killer app" for quantum computing. We will introduce quantum algorithms, with focus on a recent quantum computational model that will be familiar to researchers with a background in graphical models. We will show how a particular quantum algorithm -- quantum annealing -- running on current quantum hardware can be applied to certain optimization problems arising in machine learning. In turn, we will describe a number of challenges to further progress in quantum computing, and suggest that machine learning researchers may be well-positioned to drive the first real-world applications of quantum annealing. |
关 键 词: | 量子计算; 整数分解; 计算模型 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-18:dingaq |
阅读次数: | 67 |