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Quantum Enhanced Inference in Markov Logic Networks
University of Borås, Faculty of Librarianship, Information, Education and IT.ORCID iD: 0000-0002-1539-8256
2016 (English)In: arXiv, 1611.08104Article in journal (Other academic) Submitted
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. 1611.08104
National Category
Subatomic Physics
Identifiers
URN: urn:nbn:se:hb:diva-11642OAI: oai:DiVA.org:hb-11642DiVA: diva2:1062335
Available from: 2017-01-05 Created: 2017-01-05 Last updated: 2017-03-17Bibliographically approved

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Wittek, Peter

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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