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.
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.
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.
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.
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) is an emerging interdisciplinary field which incorporates some features of quantum computing with machine learning. Quantum hardware development has sparked research interest for AI scientists because it gives capabilities in high-dimensional data processing and simultaneous operation execution. The investigation within this paper traces the main features of QML by reviewing variational quantum classifiers (VQCs) as well as quantum kernels and hybrid quantum-classical models. VQCs serve as quantum parameterized circuits whose optimization process with classical feedback achieves quantum superiority for classification applications. Quantum kernels expand Hilbert space features to improve traditional kernel methods, thus proving their functionality in quantum feature space. Hybrid approaches unite NISQ hardware with classical systems, which makes QML applicable to real-world applications right now. (Rietsche et al., 2022; Tychola et al., 2023) An analysis explores separate functions and combined effects of these components, which enhance model performance, extend generalization, and boost computational efficiency. The paper discusses necessary knowledge first, along with existing applications, before analyzing them against traditional methods. The paper reviews current QML tools while exploring their operational readiness as well as practical issues and deployment barriers for broader adoption. The paper conducts an in-depth investigation of significant limitations that include hardware noise alongside questions regarding scalability and interpretability. This paper shows how QML will transform machine learning applications through its review of obstacles that must be resolved to achieve its complete potential development. The study presents researchers and practitioners with an extensive comprehension of QML developments and emerging paths for this revolutionary field.
Quantum computing has an enormous potential in machine learning, where problems can quickly scale to be intractable for classical computation. Quantum machine learning is research area that combines the interplay of ideas from quantum computing and machine learning. Powerful and useful machine learning is dependent on having large-scale datasets used to train the models to be able to solve real-life problems. Currently, quantum machine learning lacks a plethora of large-scale quantum datasets required to further develop the models and test the quantum machine learning algorithms. Lack of large datasets is currently limiting the quantum advantage in the field of quantum machine learning. In this thesis, the concept of quantum data and different types of applied quantum datasets used to develop quantum machine learning models is studied. The research methodology is based on a systematic and comparative literature review of the state of the art articles in quantum computing and quantum machine learning in the recent years. We classify datasets into inherent and non-inherent quantum data based on the nature of the data. The preliminary literature review addresses patterns in the applied quantum machine learning. Testing and benchmarking QML models primarily uses non-inherent quantum data, or classical data encoded into the quantum system, while separate research is focused on generating inherent quantum datasets.