期刊
SCIENTIFIC REPORTS
卷 7, 期 -, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/srep45672
关键词
-
资金
- European Research Council (CoG QITBOX and AdG OSYRIS)
- Axa Chair in Quantum Information Science, Spanish MINECO (FOQUS) [FIS2013-46768, QIBEQI FIS2016-80773-P]
- Severo Ochoa [SEV-2015-0522]
- Fundacio Privada Cellex, and Generalitat de Catalunya [SGR 874, 875]
- CERCA Programme
- European Union's Marie Sklodowska-Curie Individual Fellowships (IF-EF) programme [700140]
- Marie Curie Actions (MSCA) [700140] Funding Source: Marie Curie Actions (MSCA)
Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据