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Quantum Enhanced Inference in Markov Logic Networks
#MMPMID28422093
Wittek P
; Gogolin C
Sci Rep
2017[Apr]; 7
(?): 45672
PMID28422093
show ga
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.
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