Abstract Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms (VQAs) have been proposed and tested on QVMs to surpass the limitations of quantum hardware. Our goal is to exploit further the VQAs towards practical applications of quantum machine learning (QML) using state-of-the-art quantum computers. In this paper, we first introduce a QVM named Qsun, whose operation is underlined by quantum state wavefunctions. The platform provides native tools supporting VQAs. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of QML: quantum linear regression and quantum neural network.
Abstract Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine (QVM) that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms (VQAs) have been proposed and tested on QVMs to surpass the limitations of quantum hardware. Our goal is to exploit further the VQAs towards practical applications of quantum machine learning (QML) using state-of-the-art quantum computers. In this paper, we first introduce a QVM named Qsun, whose operation is underlined by quantum state wavefunctions. The platform provides native tools supporting VQAs. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of QML: quantum linear regression and quantum neural network.
Quantum Machine Learning (QML) integrates quantum algorithms into machine learning programs to enhance and expedite classical machine learning techniques. It leverages quantum data and hybrid quantum-classical models, utilizing the principles of superposition and entanglement to handle complex joint probability distributions that would require exponential classical computational resources. QML aims to harness the unique properties of quantum computers for practical machine learning tasks, with the goal of achieving quantum advantage over classical approaches. QML finds applications in diverse fields such as chemical and quantum matter simulation, quantum control, quantum communication networks, and quantum metrology. Quantum algorithms can analyze quantum states and improve computational speed and data storage. Two main frameworks, quantum kernel methods and variational quantum algorithms, are widely used due to their implement ability on quantum hardware and capacity to work on general datasets. Quantum computers can solve linear algebraic problems, calculate eigenvectors and eigenvalues, and perform support vector algorithms at exponentially faster rates than classical computers. Despite its potential, QML faces significant challenges related to quantum hardware capabilities, including limited qubit connectivity, noise, coherence times, and errors in state preparation and measurement. Hardware constraints, compatibility issues, high computational and memory requirements, and the difficulty of quantum programming also pose obstacles. Overcoming these hardware and software limitations is crucial for realizing the full potential of quantum machine learning
As the limits of classical machine learning (ML) become increasingly evident—especially in terms of computational scalability and data processing—quantum computing emerges as a promising frontier capable of addressing these challenges. Quantum Machine Learning (QML) represents the intersection of quantum computing and traditional ML, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance learning capabilities, accelerate training, and solve complex problems that are intractable for classical algorithms. This paper provides a comprehensive overview of the evolving field of QML, beginning with the theoretical foundations of quantum computing and its synergy with classical machine learning principles. We explore key QML algorithms, including quantum support vector machines (QSVM), variational quantum classifiers (VQC), quantum neural networks (QNN), and hybrid quantum-classical approaches that exemplify the practical integration of both paradigms. We further examine real-world applications of QML across domains such as drug discovery, finance, climate modeling, and optimization, where quantum enhancements have begun to show early but promising results. Despite its theoretical potential, QML faces substantial limitations, including quantum hardware instability, limited qubit scalability, noise sensitivity, and unresolved challenges in quantum data encoding and interpretability. In synthesizing recent advancements and persistent obstacles, this paper highlights the importance of continued interdisciplinary research, algorithmic refinement, and hardware evolution. We also outline future directions that could lead QML from experimental promise to practical utility, including developments in quantum error correction, benchmarking standards, and application-specific quantum architectures
At the intersection of two promising technologies, Machine Learning (ML) and Quantum Computing (QC), Quantum Machine Learning (QML) emerges. However, while QC is still in an early stage of development, QML is even more so. In this study, the Auto-MPG dataset was selected for its dimension, making it suitable for QML tasks within the current limitations of quantum hardware, especially regarding qubit requirements. Unlike classification problems that dominate the literature, regression problems have only been explored theoretically. This study aims to bridge that gap by applying QML techniques to a practical regression task. To this end, a preliminary analysis was conducted using a classical model as a reference point for subsequent evaluation. The quantum experimentation was performed in two phases: first, the model was trained in an ideal simulator to determine the model's best hyperparameters. Then, these best models were run in a noisy simulator using quantum error mitigation techniques. The results show how the Variational Quantum Algorithms (VQA) outperforms classical Extreme Gradient Boosting and the methods used to mitigate errors in quantum hardware are effective, as they achieve comparable results between the noisy simulator and the perfect simulator. This brings closer the possible applications in NISQ current devices.
Quantum machine learning (QML) represents a cutting-edge convergence of quantum computing and artificial intelligence, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance machine learning algorithms. By exploiting the computational advantages of quantum systems, QML offers the potential for exponential speedups in data processing, optimization, and pattern recognition tasks, particularly for high-dimensional and complex datasets. This article explores the foundational principles, algorithmic approaches, and practical applications of quantum machine learning across domains including finance, chemistry, drug discovery, optimization, and natural language processing. It discusses quantum-enhanced supervised and unsupervised learning, quantum neural networks, variational quantum algorithms, and hybrid quantum-classical architectures. Additionally, the article addresses challenges related to hardware limitations, noise, scalability, and interpretability, as well as the emerging trends in near-term quantum devices, quantum simulators, and integration with classical AI pipelines. Quantum machine learning holds the promise of revolutionizing computational intelligence, offering transformative capabilities for solving problems that are currently intractable for classical systems.
Motivated by the anticipated advantage of quantum computing in complex tasks, quantum machine learning (QML) combines quantum principles with machine learning (ML) for potential speedups. This paper reviews recent progress in three main directions: (a) Variational Quantum Algorithms (VQAs), (b) Quantum Convolutional Neural Networks (QCNNs), and (c) quantum kernel methods. Key challenges are highlighted, including data encoding, hardware noise in the Noisy Intermediate-Scale Quantum (NISQ) regime, and the importance of standardized benchmarking. Promising strategies to address these challenges include hybrid quantum-classical integration, transfer learning, and algorithm-hardware co-design. Experimental demonstrations on platforms developed by IBM, Google, and IonQ illustrate the feasibility of QML on near-term quantum devices. Looking forward, advances in encoding schemes, noise-resilient circuit design, and domain-specific applications in chemistry, materials, and biomedicine are expected to further accelerate the practical development of QML.
Recent advances in Quantum Machine Learning (QML) bring together Quantum Computing and Machine Learning with the aim of enhancing the advantages of Machine Learning to solve problems efficiently. The properties of quantum systems (superposition, entanglement, etc.) to represent multiple states of information at once, QML can potentially offer significant advantages when solving complex high-dimensional (multidimensional) computational problems. This paper reviews the current state-of-art regarding QML in general through an organized compilation and assessment of selected studies recently published in scientific literature that were found via large electronic library catalogs based on peer-reviewed sources according to algorithms used, optimization approaches employed, and areas of applied study. This review provides comparative analyses of each selected studies for purposes of identifying major trends in QML research/practice as well as opportunities for methodological improvements and challenges still existing within QML. Finally, this review presents an overview of Quantum Computing and Quantum Data Encoding Principles utilized to translate Classical Data into Quantum States as a high-level evaluation of several popular Quantum Learning Algorithms (e.g., Quantum Neural Networks, Quantum Support Machines, and Variational Quantum Circuit). The survey describes the optimization strategies such as gradient based techniques, Bayesian Optimization and evolutionary techniques being used to improve the stability of the quantum machine learning (QML) training process. Then, it provides examples of application areas for such QML, where healthcare analytics, environmental modelling, manufacturing optimization and analysis of biologic data are included. The comparative review of the recent academic research has identified several limitations and challenges regarding the use of QML that researchers. Although QML is believed to be capable of solving complex computational problems, issues such as noise in hardware, limited numbers of qubits and scalability present significant challenges for researchers working with this type of machine learning application. Overall, it appears that this survey provides a broad overview of contemporary developments and future trends for developing high-performance quantum learning systems.