Quantum Machine Learning (QML) is a discipline of research at the intersection of quantum information and machine learning that leverages quantum mechanical properties to enhance computational capabilities. With its emergence, there is a need to integrate QML models into machine learning pipelines for real-life applications such as image processing. While standalone programs exist to demonstrate the performance of QML models, a well-defined model workflow is noticeably absent. This thesis thoroughly explores various existing QML models and their practical utility in image processing tasks, with the aim of constructing a robust QML library. Throughout this thesis, we develop piQture, an open-source Python and Qiskit-based library that streamlines the development, training, and evaluation of QML models. Its design and structure prioritize usability among users familiar with classical machine learning without prior QML experience. Further, piQture is augmented with automated building, testing, and packaging workflows that enhance software reliability and reproducibility. Finally, we provide strategies to facilitate model management and storage within piQture for practical adoption and future analysis of pre-trained QML models.
Quantum Machine Learning (QML) is a discipline of research at the intersection of quantum information and machine learning that leverages quantum mechanical properties to enhance computational capabilities. With its emergence, there is a need to integrate QML models into machine learning pipelines for real-life applications such as image processing. While standalone programs exist to demonstrate the performance of QML models, a well-defined model workflow is noticeably absent. This thesis thoroughly explores various existing QML models and their practical utility in image processing tasks, with the aim of constructing a robust QML library. Throughout this thesis, we develop piQture, an open-source Python and Qiskit-based library that streamlines the development, training, and evaluation of QML models. Its design and structure prioritize usability among users familiar with classical machine learning without prior QML experience. Further, piQture is augmented with automated building, testing, and packaging workflows that enhance software reliability and reproducibility. Finally, we provide strategies to facilitate model management and storage within piQture for practical adoption and future analysis of pre-trained QML models.
Quantum Machine Learning (QML) leverages quantum mechanical properties to enhance computational capabilities. With its emergence, there is a need to integrate QML models into machine learning pipelines for real-life applications such as image processing. While standalone programs exist to demonstrate the performance of QML models, a well-defined model workflow is noticeably absent. We present piQture, an open-source Python and Qiskit-based library that streamlines the implementation and training of QML models tailored for image processing. Its design and structure prioritize usability among users familiar with classical machine learning without prior QML experience.
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce AutoQML, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry, and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum systems, such as superposition and entanglement, to process information in ways that are not possible with classical computers. This paper provides a comprehensive overview of QML, including its principles, algorithms, and applications. We focus particularly on supervised learning methods, which involve training a quantum model on labeled data to make predictions on new, unseen data. We discuss the potential of QML to revolutionize various domains, such as finance, chemistry, and materials science, and highlight the challenges associated with the development and implementation of QML algorithms, including the need for more advanced quantum hardware and software. This paper aims to provide a clear understanding of the current state of QML research and its potential impact on future computational capabilities.
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.
Among notable applications, quantum machine learning (QML) is a promising task that combines quantum computing with machine learning, cited for various types of complex computational problems. This chapter briefly summarizes the frameworks, software platforms, and toolchains currently available for implementing, simulating, and executing hybrid quantum–classical learning algorithms. Major frameworks (e.g., Qiskit Machine Learning, PennyLane, TensorFlow Quantum, and the PyTorch-based toolsets) are analyzed in detail based on their architectural design, functionality, and integration capabilities. They have several key features, such as variational quantum circuits, parameterized quantum gates, automatic differentiation capabilities, and easy integration into a classical deep learning ecosystem. The chapter groups together ongoing research in different architectural paradigms, practical usability aspects, and empirical performance tests. Additionally, it explores aspects of scalability, adaptability to hardware, noise resistance, and reproducibility. The results bring to light elements of the design of trade-offs for creating robust and efficient toolchains for QML, a QML best practice, and guidance for future QML design.