Quantum Circuit For Discovering from Data the Structure of Classical Bayesian Networks

Abstract

We give some quantum circuits for calculating the probability $P(G|D)$ of a graph $G$ given data $D$. $G$ together with a transition probability matrix for each node of the graph, constitutes a Classical Bayesian Network, or CB net for short. Bayesian methods for calculating $P(G|D)$ have been given before (the so called structural modular and ordered modular models), but these earlier methods were designed to work on a classical computer. The goal of this paper is to "quantum computerize" those earlier methods.

20 pages (9 files: 1 .tex, 2 .sty, 6 .eps)

References in corpus (4)

• Advances in exact Bayesian structure discovery in Bayesian networks
• Quantum Circuit for Calculating Symmetrized Functions Via Grover-like Algorithm
• Quibbs, a Code Generator for Quantum Gibbs Sampling
• Quantum Circuit for Calculating Mobius-like Transforms Via Grover-like Algorithm

Cited by in corpus (1)

• Bayesian Network Structure Learning Using Quantum Annealing