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Lecture notes in computer science

An Optimized Quantum Circuit Representation of Bayesian Networks

Walid Fathallah, Nahla Ben Amor, Philippe Leray

Published Nov 18, 2023• 1 citation
Publisher
DOI
10.1007/978-3-031-45608-4_13

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•Aug 31, 2026•0 citations
Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis. We consider a 4-node BN for stock prediction for our experimental evaluation. We construct a quantum circuit to represent the 4-node BN using Qiskit, and run the circuit on nine IBM quantum devices: Yorktown, Vigo, Ourense, Essex, Burlington, London, Rome, Athens and Melbourne. We will also compare the performance of each device across the four levels of optimization performed by the IBM Transpiler when mapping a given quantum circuit to a given device. We use the root mean square percentage error as the metric for performance comparison of various hardware.
The Adaptive Bayesian Networks (ABN) represent a powerful framework for modeling complex systems by leveraging probabilistic inference and Bayesian reasoning. This paper introduces a novel quantumized algorithm for ABN, designed to enhance learning and prediction accuracy, particularly for systems exhibiting intricate probability distributions. We explore the utilization of quantum phenomena – superposition and entanglement – to accelerate the network's learning process and improve the precision of risk assessments. The core mechanism focuses on applying quantum-enhanced optimization techniques to the ABN structure, leading to a more efficient and accurate representation of the underlying probability landscape. This research addresses the limitations of classical ABN algorithms, offering a promising pathway toward enhanced predictive capabilities in diverse domains.