Quantum-Inspired Machine Learning (QiML) is a subfield of Quantum Machine Learning (QML) that implements quantum mechanical principles using classical computational resources, without requiring quantum hardware. Despite growing interest, QiML is frequently treated as a subset of broader QML surveys, leaving a gap in unified, cross-domain coverage. This survey provides the first dedicated examination of QiML as a standalone field, reviewing literature from 2017 to 2025 across tensor network simulations, dequantized algorithms, and variational quantum circuit simulations. We propose a unified taxonomy spanning these families, survey empirical applications across diverse domains, and assess practical limitations and open problems. Our analysis reveals that algorithmic maturity and practical readiness vary considerably across QiML families, with domain-specific success patterns driven by differing structural assumptions. Fundamental open questions remain around scalability, data embedding, and the relationship between quantum inductive biases and learning efficiency. This survey serves as a reference for researchers seeking a structured understanding of QiML’s current landscape and future directions.
Quantum-Inspired Machine Learning (QiML) is a subfield of Quantum Machine Learning (QML) that implements quantum mechanical principles using classical computational resources, without requiring quantum hardware. Despite growing interest, QiML is frequently treated as a subset of broader QML surveys, leaving a gap in unified, cross-domain coverage. This survey provides the first dedicated examination of QiML as a standalone field, reviewing literature from 2017 to 2025 across tensor network simulations, dequantized algorithms, and variational quantum circuit simulations. We propose a unified taxonomy spanning these families, survey empirical applications across diverse domains, and assess practical limitations and open problems. Our analysis reveals that algorithmic maturity and practical readiness vary considerably across QiML families, with domain-specific success patterns driven by differing structural assumptions. Fundamental open questions remain around scalability, data embedding, and the relationship between quantum inductive biases and learning efficiency. This survey serves as a reference for researchers seeking a structured understanding of QiML’s current landscape and future directions.
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 Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.
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 combines quantum computing with machine learning to solve complex computational problems more efficiently than classical approaches. This survey provides an introduction to the foundations, algorithms, frameworks, data and applications of quantum machine learning, serving as a resource for researchers and practitioners. We begin by reviewing existing surveys to identify gaps that this work addresses, followed by a detailed discussion of the foundational principles of quantum mechanics and machine learning essential for quantum machine learning. Key algorithms are examined, highlighting their mechanisms, advantages, and applications across various domains. Current frameworks and platforms for implementing quantum machine learning algorithms are explored, emphasizing their unique features and suitability for different contexts. Existing quantum datasets for practical usage are also reported and commented on. This survey also reviews over 135 articles, categorized into theoretical and practical contributions, to identify key advances, limitations, and application areas within quantum machine learning. Critical challenges such as hardware limitations, error rates, and scalability are analyzed to detect the obstacles that must be addressed for practical deployment. By synthesizing these elements into a structured overview, this survey aims at serving as both an introduction and a guide for advancing research and development in this disruptive field.
Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.
Quantum machine learning (QML) has emerged as a transformative paradigm that leverages quantum mechanical phenomena superposition, entanglement, and quantum interference to enhance computational approaches in healthcare. This comprehensive survey systematically examines advances in QML for healthcare applications from 2020 to 2025, analyzing cutting-edge algorithms, recent implementations, and emerging prospects across drug discovery, medical imaging, genomics, and clinical decision support. We review Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and hybrid quantum-classical frameworks. Our analysis of 150+ recent publications reveals significant theoretical progress alongside persistent hardware constraints and algorithmic challenges. We provide a comprehensive taxonomy of QML algorithms, a comparative performance analysis against classical baselines, a detailed examination of NISQ-era limitations, and a technology roadmap spanning near-term hybrid approaches through long-term fault-tolerant quantum computing. This survey serves as an updated reference for researchers and practitioners navigating the rapidly evolving intersection of quantum computing and medical informatics.