Experimental evaluation of quantum Bayesian networks on IBM QX hardware

TL;DR

This paper experimentally evaluates the performance of Quantum Bayesian Networks on IBM QX quantum hardware using a stock prediction model, comparing results with simulators and classical methods to assess quantum hardware effectiveness.

Contribution

It presents the first experimental assessment of QBN performance on real quantum hardware, analyzing multiple IBM devices and optimization levels for probabilistic inference tasks.

Findings

Quantum hardware shows varying accuracy levels for QBN tasks.

Optimization levels impact the performance of quantum circuits.

QBN implementation on IBM QX provides insights into practical quantum advantage.

Abstract

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.