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

TL;DR

This paper introduces quantum circuits to compute the probability of Bayesian network structures given data, aiming to adapt classical Bayesian methods for quantum computing to enhance efficiency.

Contribution

The paper presents quantum circuits that implement classical Bayesian network structure learning methods on quantum computers, bridging classical probabilistic models with quantum algorithms.

Findings

Quantum circuits for calculating P(G|D) are proposed.

The approach aims to 'quantum computerize' existing classical Bayesian methods.

Potential for more efficient Bayesian network structure discovery using quantum computing.

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.