Quality prediction and inspection in industrial engineering, as it helps ensure that manufactured products meet consistent quality standards, allows for more efficient allocation of resources, ensures that products meet industry standards, regulatory requirements, and safety regulations, detects deviations from quality standards early in the production process, allowing for prompt corrective actions, aids in production planning and scheduling, fosters a culture of continuous improvement, reduces the cost of rework, warranty claims, and customer returns, and increases customer satisfaction. In this module, we will analyze a quality prediction dataset by applying a Quantum Logistic Regression (QLR), where we define parameters such as qubits and apply various packages and functions such as qnode and KerasLayer.
Learning objectives: after completing this module, students will be able to
(i) describe the quality prediction and inspection and its types
(ii) explain the importance of quality prediction and inspection
(iii) apply the knowledge learned in this module to analyze, predict, and inspect product quality using various quantum machine learning models