Monitoring and Evaluating Quantum Generative Models Using Spark and MLflow

Quantum generative models (QGMs), including Variational Quantum Circuits (VQCs) and Quantum GANs, hold significant potential in generating complex data distributions beyond the capabilities of classical generative approaches. However, robust monitoring and evaluation of QGMs remain underdeveloped due to hardware constraints, stochastic quantum behavior, and reproducibility limitations. This paper proposes a scalable and modular framework using Apache Spark and MLflow to monitor, evaluate, and track the performance of QGMs. The framework enables ingestion of quantum-generated data, distributed computation of performance metrics such as fidelity, entanglement entropy, and distributional divergence, and experiment tracking via MLflow. I validate our methodology using Qiskit-based simulated QGMs and demonstrate the effectiveness of classical big data tools in bridging the evaluation gap in quantum ML research.