Quantum Machine Learning for Engineering Applications

Barclay Barnes, Anna Zharikov, Meriem Hamzi

Quantum machine learning (QML) has emerged as a promising method for overcoming the computational limitations of classical machine learning when analyzing large and complex data sets. This project investigates the application of QML algorithms to real-world science and engineering problems, with a focus on civil and environmental engineering datasets. We develop and evaluate 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 proposed 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 using accuracy, computational efficiency, and scalability. The results highlight scenarios in which QML demonstrates a potential advantage over a classical approach, while also identifying the current limitations that come with near-term quantum devices. This work contributes 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.

Kartik Sharma, VidhyaLakshmi. M, Kumaran K +1 more

Quantum Machine learning (QML) is the combination of computational power of quantum computing and machine learning, which forms a highly complex system design. The hybrid nature of quantum-classical interaction as well as hardware-specific constraints in the field of quantum computing, poses unique challenges in understanding and designing quantum machine learning systems. This Study presents a structured approach in designing and modeling quantum machine learning systems using widely used software engineering practices by utilizing the Unified Modeling Language (UML) diagrams. Different UML diagrams, including use case diagram, sequence diagram, and Class diagram, are incorporated in this study to capture essential components and workflows in the quantum machine learning system and their applications. A requirement engineering lifecycle diagram is also included in this study to incorporate and understand how software engineering practices are applied to quantum computing. Through various UML diagrams, this study aims to illustrate how these models enhance modularity and development planning. To ground the modeling approach, a disease prediction system is utilized as a demonstrative example in all the modelling diagrams. This work aims to provide a foundational framework that can help and assist researchers and developers to design, communicate, and evolve quantum machine learning systems effectively and with a better and clearer vision.

Priyanka Jammwal

A. S. Wilson

Yong Shi, Dan Chia-Tien Lo, Valentina Nino +3 more

Quantum-based machine learning (QML) is known to integrate quantum computing (QC) and machine learning (ML) to boost performance in data analysis and decision-making. The demand for professionals in QML has increased; however, QML is not part of the curricula for most colleges and universities. In this project, we create labware in Google Colab that encompasses knowledge of QC, ML, and QML, and demonstrates QML applications for analyzing scientific and engineering data. We integrate our learning modules into courses in Computer Science, Industrial Engineering, Physics, and other disciplines, and host a faculty workshop, a student camp, and a conference tutorial session to promote the use of our learning modules. The feedback from participating faculty and students is overwhelmingly positive.