  Advances in data collection using inexpensive sensors have enabled monitoring
the performance of dynamic systems, and to implement appropriate control
actions to improve their performance. Moreover, engineering systems often
operate under uncertain conditions; therefore, the real-time decision-making
framework should not only consider real-time sensor data processing but also
several uncertainty sources that may impact the performance of dynamic systems.
In this paper, we investigate the modeling of such time-dependent system
behavior using a dynamic quantum Bayesian network (DQBN), which is the quantum
version of a classical dynamic Bayesian network (DBN). The DBN framework has
been extensively used in various domains for its ability to model stochastic
relationships between random variables across time. The use of the quantum
amplitude amplification algorithm provides quadratic speedup for inference and
prediction in Bayesian networks. In this paper, we combine the modeling
capabilities of DBN with the computational advantage of quantum amplitude
amplification for efficient modeling and control of time-dependent systems. We
implement the proposed DQBN framework on IBM Q hardware, and compare its
performance with classical DBN implementation and the IBM Qiskit simulator.
