This survey provides a comprehensive overview of the theoretical foundations, unified taxonomy, and key methodologies in QML, and discusses quantum data encoding techniques, variational quantum models, neural-inspired quantum architectures, and quantum data learning approaches, along with their associated challenges and limitations.
Abstract
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary field that combines quantum computing with machine learning to address complex computational problems. This survey provides a comprehensive overview of the theoretical foundations, unified taxonomy, and key methodologies in QML. We discuss quantum data encoding techniques, variational quantum models, neural-inspired quantum architectures, and quantum data learning approaches, along with their associated challenges and limitations. The survey further examines evaluation strategies, the concept of quantum advantage, and the role of current quantum hardware and software platforms in advancing QML research. In addition, major application domains and the growing importance of trustworthy QML, including interpretability, robustness, and security, are explored. Finally, we highlight open challenges and future research directions to provide insights into the evolving landscape of Quantum Machine Learning.
Quantum Machine Learning (QML) is a new multidisciplinary field that exploits the computational power of quantum computing for machine learning problems. Current research provides an overview of important QML algorithms, such as Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Variational Quantum Eigensolvers (VQE), Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantumclassical computing techniques, which have recently become more popular owing to hardware constraints in the NISQ period. Analysis reveals that QML provides an edge in working with high-dimensional data, optimization, and probabilistic modeling. Applications of the technology include various areas such as diagnostics, drug discovery, financial models, cybersecurity, and quantum cryptography (Quantum Key Distribution, QKD). Nevertheless, QML technology suffers from multiple difficulties, including the lack of scalable qubits, noise and decoherence, inefficient encoding methods, absence of standard benchmarking, and interpretability of models. Moreover, hybrid computing architecture and nascent quantum software development are also barriers.
J. Chen· Recent Research Reviews Jour...· 0 citations
A conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning is presented.
Mohammad Wali Khurami, Musawer Hakimi· Buana Information Technology...· 0 citations
This study applied quantum circuit learning, a commonly used hybrid quantum-classical machine learning algorithm, to a machine learning interatomic potential (MLIP) for predicting the energies of molecules in molecular datasets. We retrained the ANI model using the quantum transfer learning architecture [Mari et al., Quantum, 4:340, 2020] and evaluated numerical accuracy with a quantum circuit simulator. The evaluation confirmed that inserting a quantum circuit into the classical neural network of the MLIP yielded slightly higher accuracy than the fully classical neural network under certain conditions. In particular, the model incorporating a quantum circuit was more effective when the pretraining model had room for improvement in accuracy. These findings may contribute to advancing the application of quantum machine learning for MLIPs.
Kohei Numata, Wataru Mizukami, K. Mitarai et al.· 0 citations
This review of hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.
Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin et al.· 0 citations
Quantum computing leverages quantum phenomena such as superposition and entanglement to solve complex optimization and data analytics problems more efficiently than classical computing. Recent advances in quantum algorithms, including QAOA, Quantum Annealing, VQE, and hybrid quantum-classical models, have enabled applications in finance, healthcare, logistics, manufacturing, cybersecurity, and artificial intelligence. This paper surveys quantum computing applications, proposes a taxonomy, and presents a hybrid quantum-classical framework integrating quantum optimization with quantum-enhanced machine learning. Mathematical formulation, algorithmic representation, and experimental evaluation demonstrate improved optimization accuracy, computational efficiency, predictive performance, and solution quality over conventional approaches, highlighting quantum computing's potential for next-generation intelligent decision-making systems.
John McCarthy, M. Minsky· International Journal of Mod...· 0 citations
The development of quantum technologies opens new possibilities for the optimization of machine learning (ML) algorithms. In this contribution, we report on the experimental use of quantum computing for training ML algorithms and models designed to be deployed for inference on embedded systems with limited computational resources. Due to the small size of resource-efficient models for embedded systems, this application of quantum computing provides an excellent opportunity to evaluate a technology that is currently being developed in the noisy intermediate-scale quantum era. We develop an approach based on transforming classical supervised learning problems into a discrete form, enabling their solution using quantum annealing (QA) methods. Three models are presented: a binary XNORNet network, a support vector machine (SVM) with a discrete representation of dual coefficients, and a convolutional neural network. All experiments are performed on the MNIST dataset, using classical optimization methods, simulated annealing, and QA on the D-Wave Advantage system. While classical gradientbased methods maintain superior absolute accuracy (up to 95.97% for SVM), the D-Wave system demonstrates a significant reduction in core optimization time, achieving speedups of up to 15.6× compared to classical optimization on a central processing unit when the system overhead is excluded. Preliminary results suggest that while QA entails a slight accuracy and memory requirements penalty, a drastic reduction in active annealing duration serves as a promising strategy to reduce training latency in future embedded-focused ML applications.
Michał Mańkowski, Bartosz Zwoliński, Arkadiusz Lewandowski et al.· International Conference on...· 0 citations
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