The current review's goal is to examine the strategic significance of Quantum Machine Learning (QML) as a technology tool or strategic enabler of Industry 5.0, the approaching industrial revolution. To summarize and categorize the most common forms, antecedents, and outcomes of sustainable innovation, the primary objective of this study is to systematically identify and critically evaluate the literature on sustainable innovation in Industry 5.0 since January 2020. A thorough search and review of 5984 scholarly articles were identified during the systematic review, and 36 publications were chosen by a cyclic filtration for a careful review in the fields of Industry 5.0 and QML to analyze the articles' contents. The findings demonstrate that QML provides a revolutionary improvement in decision-making platforms, system security, and data analytics in Industry 4.0. By taking into consideration a methodical and critical assessment of the research manuscripts, the proposed survey offers readers an impactful insight into the functions of quantum machine learning as an enabler to Industry 5.0 processes. QML is emphasized as a key driver for secure, intelligent, and sustainable industrial ecosystems through QML-assisted designs across various verticals, such as smart healthcare, manufacturing, digital twins, and Cobots, as this review methodically examines the enabling technologies shaping the Industry 5.0 vision. It also highlights how QML can improve decision-making, ensure post-quantum security, optimize resources, and speed up the realization of human-centric, energy-efficient, and resilient Industry 5.0 systems as quantum computing advances.
The current review's goal is to examine the strategic significance of Quantum Machine Learning (QML) as a technology tool or strategic enabler of Industry 5.0, the approaching industrial revolution. To summarize and categorize the most common forms, antecedents, and outcomes of sustainable innovation, the primary objective of this study is to systematically identify and critically evaluate the literature on sustainable innovation in Industry 5.0 since January 2020. A thorough search and review of 5984 scholarly articles were identified during the systematic review, and 36 publications were chosen by a cyclic filtration for a careful review in the fields of Industry 5.0 and QML to analyze the articles' contents. The findings demonstrate that QML provides a revolutionary improvement in decision-making platforms, system security, and data analytics in Industry 4.0. By taking into consideration a methodical and critical assessment of the research manuscripts, the proposed survey offers readers an impactful insight into the functions of quantum machine learning as an enabler to Industry 5.0 processes. QML is emphasized as a key driver for secure, intelligent, and sustainable industrial ecosystems through QML-assisted designs across various verticals, such as smart healthcare, manufacturing, digital twins, and Cobots, as this review methodically examines the enabling technologies shaping the Industry 5.0 vision. It also highlights how QML can improve decision-making, ensure post-quantum security, optimize resources, and speed up the realization of human-centric, energy-efficient, and resilient Industry 5.0 systems as quantum computing advances.
Classification is a fundamental aspect of leveraging big data for decision-making across domains such as engineering, medicine, economics, and beyond. This systematic review explores the application of quantum machine learning (QML) to address the classification challenges. Despite the rising popularity of QML, challenges persist regarding its practical efficacy and applicability relative to classical methods. This study seeks to classify and analyse the application of QML across diverse domains. Employing a systematic methodology, a total of 23 research articles were chosen after applying rigorous selection criteria between the years 2013 to 2023. At first, 520 research articles were identified, and narrowed down to 296 papers, the abstracts of the papers have been read, 61 full-text papers have been studied, and ultimately 23 papers have been selected for review. A methodological approach was employed, involving the selection of relevant papers from reputable databases, evaluation of these papers, and the subsequent derivation of insights into the performance of QML in practical classification applications. Findings indicate that QML classifiers mostly achieved superior performance compared to conventional methods across accuracy, high-dimensional data management, and computational efficiency. However, challenges remain, including the limitations of noisy quantum hardware and the requirement for hybrid and innovative quantum algorithms. Analysis of current trends reveals significant progress in hybrid quantum-classical computational methods. To conclude, this analysis reveals QML’s revolutionary capabilities while noting its current shortcomings. This study provides a thorough overview for researchers investigating QML’s application to real-world classification problems, and it offers guidelines for future research endeavors.
Quantum computing is increasingly seen as a disruptive technology capable of expanding the computational limits of advanced manufacturing systems within the emerging Industry 5.0 framework. By utilizing quantum mechanical principles such as superposition, entanglement, and quantum parallelism, quantum computation enables new approaches to solving complex optimization, simulation, and data-intensive problems that are challenging or impractical for classical computers. This paper offers a comprehensive and critical review of the potential impacts of quantum computing on advanced manufacturing, focusing on intelligent production planning, supply chain optimization, materials discovery, predictive maintenance, and human–machine collaboration, key aspects of Industry 5.0. The originality of this review lies in its integrated analysis of quantum computing alongside artificial intelligence, digital twins, and cyber–physical systems, highlighting how these technologies, when combined, improve decision-making speed, process efficiency, and sustainability. Despite these opportunities, the integration of quantum computing into Industry 5.0 systems faces critical challenges, including hardware limitations, algorithm scalability, data security concerns, workforce readiness, and the complexity of integrating quantum solutions with existing industrial infrastructures. The role of hybrid quantum-classical architectures is examined as a feasible and transitional approach for near-term manufacturing applications. By critically assessing both technological strengths and practical constraints, this review positions quantum computing as a promising enabler of resilient, human-centered, and sustainable manufacturing ecosystems. The insights aim to assist researchers, industry players, and policymakers in strategically managing the integration of quantum technologies as manufacturing systems advance toward Industry 5.0.
Quantum Machine Learning (QML) is an emerging field with significant potential to transform oncology. This systematic literature review evaluates the current landscape of QML applications in cancer research, analyzing 21 primary research articles, selected from 410 initially identified, published between 2020 and 2024. The review categorizes QML applications, assesses evidence for quantum advantage, identifies technical challenges, and outlines future directions. Findings indicate robust research activity, particularly in China and India, with hybrid quantum-classical algorithms showing promise in simulated environments for tasks like diagnosis and prediction, especially with small or imbalanced datasets. However, translation into clinical practice remains constrained by hardware limitations and the lack of rigorous real-world validation, resulting in a persistent gap between simulation and clinical application. In addition, most studies adopt quantitative experimental approaches, with limited exploration of qualitative perspectives such as usability, interpretability, and workflow integration. To address these gaps, future research should pursue standardized frameworks, mixed-method evaluation protocols, and cross-disciplinary collaboration between quantum scientists, clinicians, and implementation researchers. Such efforts would provide robust, comparable evidence of clinical utility, ensure trustworthiness in deployment, and accelerate the transition of QML from experimental promise to transformative impact in oncology.
Utilizing the capabilities of both human-centred artificial intelligence and quantum computing, quantum artificial intelligence transforms how firms operate in Industry 5.0. This enables you to perform complex simulations, optimize fast, and make smarter decisions for robust, customized, and long-lasting systems. This is particularly evident in fields where quantum computing can manage massive information and complex patterns that traditional computer cannot, such as drug development, logistics, and new materials. It assists Industry 5.0 in achieving its objectives, which go beyond automation, which was the main focus of Industry 4.0. It does this via fostering sustainability, developing stress-tolerant systems, and facilitating human-machine collaboration (Augmented Intelligence). The following are some of Industry 5.0's most significant effects: optimization Logistics, supply chain optimization, and industrial optimization may all be greatly accelerated with the use of quantum machine learning (QML). Over time, this improves productivity and reduces waste. More precise predictive maintenance and complex analysis are made possible by quantum artificial intelligence, freeing up workers to focus on more worthwhile and innovative projects. This facilitates genuine collaboration between humans and AI. Researchers can more easily concentrate on potential compounds when they model the interactions between molecules. This facilitates the development of novel medications and antibiotics. Drug development and healthcare are two industries that employ this technology. Complex quantum simulations are used in advanced materials and simulation to expedite the discovery and development of novel materials. Real-time threat detection and quantum-resistant encryption can improve the security of factory networks. By using a lot of data to determine what each individual needs, personalization enables you to create experiences and products that are quite distinct for each person. Quantum algorithms allow you to do more in less time. Additionally, quantum-inspired techniques can improve the performance of standard algorithms to provide superior outcomes. Large, extremely complicated datasets can be processed by quantum computers. This enables them to identify subtle patterns that conventional AI is unable to detect and train AI models far more quickly. Keeping up with the latest algorithms and hardware limitations is never easy. It is crucial to consider ethical concerns and ensure that technology is accessible to all people worldwide. By fusing human creativity with machine intelligence, the integration might result in the creation of more intelligent and independent systems. This could contribute to a more stable future.
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.
Quantum Machine Learning (QML), a confluence of quantum computing and classical machine learning, represents a revolutionary paradigm with transformative potential. This systematic review explores the landscape of QML by investigating its underlying principles, methodologies, diverse applications, challenges, and ethical considerations. Beginning with an examination of fundamental quantum computing principles, the review navigates through various QML methodologies, comparing them with classical counterparts. Real-world applications, ranging from quantum-enhanced optimization to drug discovery, are scrutinized, showcasing the practical implications of QML across industries. The paper systematically identifies challenges, including quantum hardware constraints and ethical considerations, while offering insights into current limitations and future research directions. A comparative analysis benchmarks QML against classical machine learning approaches, providing a nuanced understanding of its strengths and limitations. Ethical considerations underscore the importance of responsible AI practices in the integration of QML. The review concludes by identifying research gaps and suggesting future directions, emphasizing the need for continued exploration in this dynamic intersection of quantum computing and machine learning. This comprehensive exploration serves as a valuable resource for researchers, practitioners, and decision-makers seeking insights into the current state and transformative potential of Quantum Machine Learning.