Bayesian Network Structure Learning Using Quantum Annealing

15 Jul 2014Bryan O'GormanAlejandro Perdomo-OrtizRyan BabbushAlan Aspuru-GuzikVadim Smelyanskiy

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 $\mathcal O(n^2)$ qubits for $n$ Bayesian network variables... (read more)

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