Machine Learning (ML) serves as a general-purpose, highly adaptable, and versatile framework for investigating complex systems across domains. However, the resulting computational resource demands, in terms of the number of parameters and the volume of data required to train ML models, can be high, often prohibitive. This is the case in astrophysics, where multimedia space data streams usually have to be analyzed. In this context, quantum computing emerges as a compelling and promising alternative, offering the potential to address these challenges in a feasible way. Specifically, a four-step quantum machine learning (QML) workflow is proposed encompassing data encoding, quantum circuit design, model training and evaluation. Then, focusing on the data encoding step, different techniques and models are investigated within a case study centered on the Gamma-Ray Bursts (GRB) signal detection in the astrophysics domain. The results thus obtained demonstrate the effectiveness of QML in astrophysics, highlighting the critical role of data encoding, which significantly affects the QML model performance.
Machine Learning (ML) serves as a general-purpose, highly adaptable, and versatile framework for investigating complex systems across domains. However, the resulting computational resource demands, in terms of the number of parameters and the volume of data required to train ML models, can be high, often prohibitive. This is the case in astrophysics, where multimedia space data streams usually have to be analyzed. In this context, quantum computing emerges as a compelling and promising alternative, offering the potential to address these challenges in a feasible way. Specifically, a four-step quantum machine learning (QML) workflow is proposed encompassing data encoding, quantum circuit design, model training and evaluation. Then, focusing on the data encoding step, different techniques and models are investigated within a case study centered on the Gamma-Ray Bursts (GRB) signal detection in the astrophysics domain. The results thus obtained demonstrate the effectiveness of QML in astrophysics, highlighting the critical role of data encoding, which significantly affects the QML model performance.
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming to evaluate the effectiveness and limitations of quantum-enhanced models compared to classical approaches. As quantum technologies continue to develop, they offer the potential for solving complex computational problems more efficiently, particularly in fields requiring high-dimensional data processing. A literature review is conducted to contextualize current advancements in Quantum Machine Learning, highlighting theoretical benefits and existing practical limitations. This work investigates whether quantum methods, specifically the Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces traditional kernel functions with quantum kernels computed via quantum circuits and feature maps, embedding classical data into a Hilbert space. The experimental portion of this thesis compares classical and quantum models on two distinct datasets: one from the healthcare domain and another from particle physics. Various parameters are tested in a constrained grid search due to the high computational demands of quantum circuits. Results indicate that while quantum models are significantly more resource-intensive, they can achieve comparable or even improved performance on data that has more complex relations between their variables. However, scalability remains a major challenge. This work concludes that although QSVC does not yet consistently outperform classical models, it offers advantages in specific contexts and lays the groundwork for identifying future use cases where quantum computing may offer a practical advantage in ML.
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.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics
Quantum machine learning (QML) is an emerging interdisciplinary field that employs quantum computing (QC) principles to address complex computational challenges. Traditional machine learning (ML) and deep learning (DL) models face major challenges in Earth observation (EO) due to the increasing volume and complexity of data. These challenges include computational intensity, energy consumption, and data management constraints. QML emerges as a promising paradigm for overcoming these barriers through quantum phenomena such as superposition, entanglement, and interference. This review aims at offering a detailed analysis of the state of the art and new trends in QML application to EO. This study methodically examines key advancements in QML for EO (QML4EO) through an exploration of the fundamental concepts of QML. The proposed review methodology involved systematic searches across prominent scientific databases, based on our extensive knowledge on the topic, using carefully formulated queries to ensure broad coverage and high relevance. In addition, this work addresses the changing institutional and geographic environment of QML4EO research, highlighting important centers of contribution and the important role of programs such as the IEEE Geoscience and Remote Sensing Society (GRSS) Quantum Earth Science and Technology (QUEST) Technical Committee. To transform QML from a specialized research area into a complementary and essential tool for advancing EO, this article critically analyzes current limitations and provides an outlook on future directions, highlighting the need for reliable hardware, improved algorithmic design, and standardized protocols.
Quantum machine learning (QML), an emerging discipline with applications in various domains, has the potential to dramatically improve deep learning while reducing model complexity. Quantum computers are well suited for machine learning (ML) tasks, especially because ML models are rapidly growing and require large amounts of data to learn. For example, classical computers that train large language and foundation models typically require supercomputers. If a need exists to double the number of model parameters, one will have to double the size of the supercomputer needed to train it. A quantum computer's parameter or state space grows exponentially with the number of qubits. This means doubling the number of model parameters only requires adding one quantum bit. Another advantage is that quantum computers can use entanglement and interference to potentially learn patterns with less data. Currently, no existing solutions compares the performance of classical and quantum machine learning algorithms for a workflow comprised of multiple ML tasks. This poster presents a QML workspace to explore and verify these QML algorithms with different available quantum frameworks such as Qisklt, CUDA Quantum, and PennyLane in a consistent, reproducible, and theoretically sound way and provide a comparison with similar classical machine learning algorithms.
Quantum Machine Learning (QML) is an advanced discipline that emerges from the combined power of machine learning and quantum computing that has the ability to address intricate challenges in several domains. The domain of quantum machine learning investigates the development and execution of quantum software with the potential to facilitate machine learning at a much superior pace compared to traditional computers. This research delves into the fundamental principles of quantum mechanics and their crucial role in quantum computing, emphasizing the potential of various quantum algorithms to surpass classical algorithms in specific computational tasks, and then methodically navigates through the quantum machine learning algorithms, offering profound insights into their application potential in revolutionizing data analysis and complex problem-solving methodologies, including their importance in the Language Learning Models (LLM) and Language Analysis Models (LAM). The study also provides insights into the various quantum platforms, encompassing both hardware and software aspects for the implementation of QML algorithms, and also explores the challenges prevalent in QML, with a particular focus on the limitations imposed by existing quantum hardware and the intricate nuances of data processing within quantum frameworks. This study contributes by presenting the basis for future research work related to the development of algorithms in the field of quantum machine learning and anticipating the far-reaching impact of QML across diverse scientific and technological domains.