A Variational Quantum Classifier for Predictive Analysis in Industrial Production
The increasing complexity and variability of modern industrial environments have made predictive analytics an essential tool for ensuring operational continuity and efficiency. This project proposes the design, implementation, and validation of a Variational Quantum Classifier (VQC) as a novel approach to predictive maintenance and anomaly detection within smart manufacturing contexts, particularly in the framework of Industry 4.0 and Industrial Internet of Things (IIoT) systems. At the core of this work lies the integration of Quantum Machine Learning (QML) into classical data-driven pipelines for predictive analysis, exploiting quantum computing’s potential to improve decision-making in industrial processes. The classifier is based on variational circuits trained to detect anomalies using time-series data collected from sensors embedded in production systems. This approach enables the early identification of potential failures, helping to minimize unplanned downtime and optimize maintenance interventions. The use of variational quantum circuits, which combine quantum processing units (QPUs) with classical optimization routines, offers an attractive model for hybrid quantum-classical computing. These circuits are particularly suited for supervised learning tasks and have shown promising results in classification problems under limited computational resources. To ensure a fair comparison, the quantum model is benchmarked against traditional classifiers, including logistic regression, random forests, and support vector machines. The project uses IBM’s Qiskit framework to simulate and run experiments on both simulators and real quantum hardware. Initial experiments demonstrate that the VQC can achieve comparable or even superior performance to classical models in small-scale settings. The classifier successfully distinguished between normal and anomalous operating conditions with high accuracy. Moreover, it showed robustness against noisy data (a common challenge in real-world industrial systems). One key insight from the study is that quantum models tend to require fewer parameters to achieve similar expressiveness as classical models, potentially reducing training time and energy consumption in future quantum-native systems. As quantum hardware continues to evolve, this work provides a foundation for deploying quantum-enhanced analytics at scale. Future developments may include:
- Expanding the dataset size and dimensionality to test the classifier’s scalability;
- Implementing alternative quantum encodings to improve data compression;
- Integrating the system into real-time monitoring environments for live anomaly detection.