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.
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.
Quantum Machine Learning (QML) emerges as a transformative approach to addressing the growing complexities of modern data processing and computational challenges. Classical machine learning (CML) techniques, while powerful, face limitations in handling vast amounts of high-dimensional data and solving complex optimization problems efficiently. Despite its potential, QML remains underrepresented in academia, highlighting the need for accessible, hands-on learning experiences and knowledgeable faculty. This project seeks to advance QML education by incorporating it into diverse curricula, creating practical learning materials, and fostering workforce readiness. Using Google Colab, an open source labware has been designed to provide interactive learning modules (M0 to M8), each addressing specific computational and industrial challenges. These modules cover key QML concepts, including Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNNs), and comparisons between QML and CML. Additionally, select modules demonstrate real-world applications of QML algorithms in areas such as predicting product back-orders, quality inspection in manufacturing, patient flow analysis in healthcare, and predictive maintenance for operational efficiency. To support the adoption of QML in academia, a faculty workshop was recently held with participants from universities in Georgia, Florida, and Missouri, representing fields such as Industrial and Systems Engineering, Computer Science, and Electrical Engineering. This workshop provided faculty with the tools and knowledge needed to integrate QML modules into their courses. A student workshop is scheduled for the end of March, offering hands-on experience and engagement with QML concepts. This project is shaping the future of QML education by providing accessible, hands-on learning experiences and fostering interdisciplinary collaboration. Through faculty and student workshops, it equips the next generation of professionals with the skills needed to drive advancements in QML across academia and industry.
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.
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.
Quantum Machine Learning (QML) is an emergent discipline that integrates the principles of quantum computing with traditional machine learning techniques, aiming to enhance the capabilities of data analysis and decision-making processes. Leveraging the unique properties, QML promises to revolutionize machine learning by offering superior processing power and computational efficiency. The synergistic approach followed by each Quantum Machine Learning Algorithm allows for the management of large databases and the execution of complex computational tasks more efficiently than classical algorithms. The integration of QML into machine learning workflows can lead to the development of advanced AI systems capable of personalized treatment recommendations, scientific discovery, and data-driven decision-making, thereby transforming the landscape of artificial intelligence and decision-making processes.
Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.