Quantum circuit representation of Bayesian networks

Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems perform various types of analysis probabilistic prediction, risk analysis, and system health monitoring, which can become computationally expensive in large-scale systems. While demonstrations true quantum supremacy remain rare, computing applications managing exploit the advantages amplitude amplification have shown significant computational benefits when compared against their classical counterparts. We develop a systematic method for designing circuit represent generic discrete network with nodes that may two or more states, where than states mapped multiple qubits. The marginal probabilities associated root (nodes without any parent nodes) represented using rotation gates, conditional probability tables non-root controlled gates. gates one control qubit ancilla proposed approach is demonstrated three examples: 4-node oil company stock 10-node liquidity assessment, 9-node naive Bayes classifier bankruptcy prediction. circuits were designed simulated Qiskit, platform enables simulations also has capability run on real hardware. results validated those obtained from implementations.