The European Physical Journal Special Topics

, Volume 224, Issue 1, pp 163–188 | Cite as

Bayesian network structure learning using quantum annealing

  • B. O’Gorman
  • R. Babbush
  • A. Perdomo-Ortiz
  • A. Aspuru-Guzik
  • V. Smelyanskiy
Part of the following topical collections:
  1. Quantum Annealing: The Fastest Route to Quantum Computation?


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 𝓞(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.


Bayesian Network European Physical Journal Special Topic Directed Acyclic Graph Directed Cycle Penalty Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© EDP Sciences and Springer 2015

Authors and Affiliations

  • B. O’Gorman
    • 2
  • R. Babbush
    • 2
  • A. Perdomo-Ortiz
    • 1
  • A. Aspuru-Guzik
    • 2
  • V. Smelyanskiy
    • 1
  1. 1.Quantum Artificial Intelligence Laboratory, NASA Ames Research Center, Moffett FieldMoffett FieldUSA
  2. 2.Department of Chemistry and Chemical BiologyHarvard UniversityCambridgeUSA

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