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April 7, 2026Computer Science Review2 citationsOpen Access

Quantum machine learning for industry 5.0: Fundamental, applications and research challenges

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ZAZulqarnain AhmedSri Sri UniversityJKJ H Hemanth KakarlaOffice of Legacy ManagementAHAbhishek HazraNational Institute of Technology Agartala

Key Points

  • The research aims to assess the role of Quantum Machine Learning and IoT in advancing Industry 5.0 while addressing ethical concerns.
  • Analyzed integration of Quantum Machine Learning with IoT technologies.
  • Proposed a five-layer Quantum IoT architecture.
  • Highlighted practical applications such as quantum digital twins and sustainable supply chains.
  • Explored ethical implications including fairness and transparency in AI.
  • Outlined how green quantum computing can enhance energy efficiency.
  • Demonstrated the potential of quantum explainability to improve ethical decision-making.
  • Identified challenges in scaling up quantum technologies while ensuring ethical integrity.

Abstract

Industry 5.0 is a paradigm shift where human-centric innovation, sustainability, and ethics are the foundation of technology development. This study examines the ability of Quantum Machine Learning (QML) and Internet of Things (IoT) integration to drive the Industry 5.0 revolution through energy efficiency, openness, and secure data management. The innovations comprise green quantum computing for energy savings, quantum explainability for enhancing ethical decision-making, and quantum blockchain for building trust in industrial networks. A five-layer Quantum IoT (QIoT) architecture is proposed, incorporating adaptive feedback and ethical governance, to align with the principles of Industry 5.0. Practical applications like optimizing sustainable supply chains, disaster resilience, and enabling Quantum Digital Twins (QDTs) for smart manufacturing are described. Scaling up nascent quantum technologies while ensuring ethical integrity is also explored in this article, and a roadmap for developing sustainable, human-centric industrial systems is presented. Furthermore, this article addresses the ethical aspects of QML in Industry 5.0, namely, guaranteeing fairness, transparency, and responsible AI use. These challenges need to be surmounted in order to align QML innovations with Industry 5.0’s people-first values.

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Cite This Study

Ahmed et al. (2026) studied this question.

synapsesocial.com/papers/69d49ecbb33cc4c35a22781dhttps://doi.org/10.1016/j.cosrev.2026.100976
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