Journal
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
Volume 224, Issue 1, Pages 163-188Publisher
SPRINGER HEIDELBERG
DOI: 10.1140/epjst/e2015-02349-9
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Funding
- AFRL Information Directorate [F4HBKC4162G001]
- NASA Advanced Exploration Systems program
- NASA Ames Research Center
- National Science Foundation [NSF CHE-1152291]
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We introduce a method for the problem of learning the structure of a Bayesian network using the quantum adiabatic algorithm. We do so by introducing an efficient reformulation of a standard posterior-probability scoring function on graphs as a pseudo-Boolean function, which is equivalent to a system of 2-body Ising spins, as well as suitable penalty terms for enforcing the constraints necessary for the reformulation; our proposed method requires oe(n (2)) qubits for n Bayesian network variables. Furthermore, we prove lower bounds on the necessary weighting of these penalty terms. The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.
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