This topical review examines a data-centric perspective in quantum machine learning (QML). Existing work has focused on circuits and model design. Data preparation and representation have received less attention. This paper addresses two questions. First, what forms of data processing are required across representative QML methods. Second, how data-centric practices affect accuracy and scalability. A taxonomy classifies QML architectures according to data-handling requirements. A structured data-to-model workflow is discussed that comprises data ingestion, abstraction, tokenization, encoding and synthetic data generation. Results from recent work indicate that data preparation likely affects performance in near-term quantum systems.
This topical review examines a data-centric perspective in quantum machine learning (QML). Existing work has focused on circuits and model design. Data preparation and representation have received less attention. This paper addresses two questions. First, what forms of data processing are required across representative QML methods. Second, how data-centric practices affect accuracy and scalability. A taxonomy classifies QML architectures according to data-handling requirements. A structured data-to-model workflow is discussed that comprises data ingestion, abstraction, tokenization, encoding and synthetic data generation. Results from recent work indicate that data preparation likely affects performance in near-term quantum systems.
Preprint Quantum machine learning (QML) is a machine learning approach that uses quantum computation to improve learning performance. Research in this area has primarily followed a model-centric approach. However, near-term quantum hardware shows that data preparation also plays an important role, and can influence the quality and scalability of the learning process. Existing QML research focuses on circuits, kernels, and variational models; however, the data requirements that shape these methods remain less explored. This topical review addresses two questions. First, what forms of data processing are required across representative QML methods? Second, how can a data-centric approach improve the accuracy, scalability and resource use in QML models? A taxonomy was introduced to classify QML architectures according to their requirements for dimensionality reduction, encoding, dataset balance and compression. Based on this taxonomy, a data-centric QML (dc-qml) framework is proposed. It integrates various subprocesses into a coherent data flow, such as data ingestion, abstraction, tokenization, quantum encoding, and synthetic data generation. Results from recent benchmarks and case studies show that improvements in near-term QML optimizations often stem from data-focused practices rather than model refinement alone.
Quantum machine learning research (QML) has largely followed a model-centric perspective that focuses on circuit design and algorithm refinement. However, near-term quantum hardware constraints place equal importance on how data are prepared and encoded before quantum processing. This short paper presents preliminary work on a data-centric quantum machine learning methodology termed dc-qml. The approach structures data ingestion, tokenization, encoding and synthetic data generation within a unified data-to-model workflow. The goal is to explore how systematic data preparation can improve the performance of QML systems. This work in progress is intended to generate discussion at the first SBCCQ workshop.
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 Quantum Machine Learning (QML) is an emerging interdisciplinary field that merges quantum computing with classical machine learning (ML) to enhance computational efficiency, scalability, and accuracy in data-driven tasks. By leveraging quantum properties such as superposition, entanglement, and quantum parallelism, QML has the potential to solve complex problems that are intractable for classical models. This survey provides a comprehensive, systematic review of recent advancements in QML, conducted following the PRISMA 2020 guidelines and covering fundamental encoding techniques that enable quantum computers to process classical data, as well as essential tools, datasets, and evaluation metrics used in quantum model development. Furthermore, it explores various QML approaches, including supervised, semi-supervised, unsupervised, and reinforcement learning, alongside quantum deep learning (QDL) models such as Quantum Neural Networks (QNNs) and Variational Quantum Circuits (VQCs). The paper also discusses the challenges in QML, including hardware limitations, scalability issues, and the need for improved quantum error correction. A dual-axis taxonomy simultaneously classifies algorithms by learning paradigm and quantum-classical integration level – a structure not found in prior surveys. The survey also covers Quantum Transfer Learning, Quantum Graph Neural Networks, and hardware-aware encoding selection, and introduces the first structured adversarial robustness taxonomy and Explainable QML treatment in a comprehensive QML survey. Together, these contributions position this work as both a reference and a methodological guide for advancing QML research and applications.
Quantum Machine Learning (QML) and Quantum Deep Learning (QDL) are emerging research areas that combine quantum computing with machine learning and deep learning techniques.These approaches aim to improve computational speed, feature representation, optimisation, and learning performance in complex data-driven problems.This review paper presents a structured discussion of recent developments in QML and QDL, focusing on major algorithms, application areas, implementation approaches, current limitations, and future research directions.The review covers important quantum-based learning methods, including quantum support vector machines, quantum k-nearest neighbours, quantum principal component analysis, quantum neural networks, variational quantum classifiers, quantum convolutional neural networks, quantum reinforcement learning, quantum generative models, and hybrid quantum-classical models.The paper also discusses how these methods are being explored in healthcare, finance, cybersecurity, communication networks, image processing, natural language processing, and optimisation.However, the review shows that most QML and QDL models are still in the early stages of research.Current challenges include noisy quantum hardware, limited qubit count, data encoding issues, circuit design problems, difficulties with model training, and a lack of standardised benchmarks.The review concludes that QML and QDL have strong future potential, but their practical success depends on improved hardware, stronger algorithms, reliable frameworks, and fair comparison with classical models.
Quantum Machine Learning (QML) has become an important research area in recent years due to its potential to overcome the computational bottlenecks faced by classical machine learning methods. In this study, only SCI/SCI-Expanded journal articles published between 2023 and 2025 and accepted as early access for 2026 are considered, and 30 such articles are systematically examined to provide a comprehensive assessment of the current state of QML methods. The reviewed literature covers variational quantum circuits, quantum kernel methods, hybrid quantum–classical models, quantum annealing approaches, federated QML architectures, data encoding strategies, and adversarial robustness analyses. The findings indicate that QML models can exhibit stronger representational capacity and higher generalization performance compared to classical methods, particularly on high-dimensional, noisy, or small sample datasets. Reported results in domains such as healthcare, chemistry, energy systems, cybersecurity, blockchain, and finance reveal the diversity of QML applications and its interdisciplinary potential. Nevertheless, the limited qubit capacity of current NISQ hardware, constraints on circuit depth, and noise-related issues remain fundamental factors that restrict the scalability of QML models. This study synthesizes methodological trends, identifies open research problems, and proposes a directional roadmap for future QML research focused on hardware-aware circuit design, efficient data encoding schemes, and robust learning frameworks.