To identify scenarios where QML performs superior to CML according to situationally relevant metrics, and also limitations of using QML versus CML in these systems. This work aims to contribute to a modular framework for learning and applying quantum machine learning and provides insight into its practical viability and application within science and engineering applications.Â
Comparative evaluations are conducted via a Python-based system, implemented in Google Colab, that integrates multiple quantum computing frameworks including: PennyLane, TensorFlow Quantum, and Qiskit to implement and compare several QML models against their classical counterparts. The system explores a range of algorithms such as Quantum Neural Networks, Quantum Support Vector Machines, Quantum Principal Component Analysis, and Quantum Logistic Regression across at least five engineering use cases, including structural health monitoring, traffic flow prediction, air quality, flood forecasting, and pollution modeling. Performance is evaluated through measurement of accuracy, computational efficiency, and scalability. These metrics are analyzed comparatively between QML and CML trials.