In recent years, there has been a significant upsurge in the interest surrounding Quantum machine learning, with researchers actively developing methods to leverage the power of quantum technology for solving highly complex problems across various domains. However, implementing gate-based quantum algorithms on noisy intermediate quantum devices (NISQ) presents notable challenges due to limited quantum resources and inherent noise. In this paper, we propose an innovative approach for representing Bayesian networks on quantum circuits, specifically designed to address these challenges.Our aim is to minimize the required quantum resource needed to implement a Quantum Bayesian network (QBN) on a quantum computer. By carefully designing the sequence of quantum gates within the dynamic circuit, we can optimize the utilization of limited quantum resources while mitigating the impact of noise. Furthermore, we present an experimental study that demonstrates the effectiveness and efficiency of our proposed approach. Through simulations and experiments on NISQ devices, we show that our dynamic circuit representation significantly reduces the resource requirements and enhances the robustness of QBN implementation. These findings highlight the potential of our approach to pave the way for practical applications of Quantum Bayesian networks on currently available quantum hardware.