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<p>Advancements in the internet of things technology have significantly improved the quality of healthcare services, particularly through the integration of artificial intelligence. However, the healthcare sector continues to face persistent challenges, such as the increasing population, the rising burden of diseases, and the limited availability of critical medical resources. These challenges are especially pronounced during medical emergencies, where the timely and optimal allocation of healthcare resources is vital. To address this pressing issue, the current research explores the use of quantum computing in conjunction with fog computing to optimize resource distribution in emergency scenarios. The study proposes a quantum-based mapping framework that utilizes quantum-inspired techniques to calculate the medical resource availability index for individual hospitals and healthcare centers. This enables real-time prediction and efficient allocation of resources during emergencies. In addition, an intelligent quantum neural network is developed to forecast the future demand for medical resources based on dynamic emergency conditions. To validate the proposed system, a 60-day controlled simulation was conducted involving four healthcare centers. The results demonstrate that the proposed technique outperforms existing state-of-the-art resource allocation methods across multiple performance metrics. It achieves a low temporal delay of 4.59&#xa0;ms with 2.15% performance gain and exhibits high quantum mapping efficacy with a precision of 96.05%, sensitivity of 96.93%, and specificity of 97.46%. The decision-making component also shows strong performance, with a precision of 92.69%, sensitivity of 91.65%, specificity of 94.26%, and an <i>F</i>-measure of 95.26%, resulting in an average performance gain of 3.25% in comparison to state-of-the-art models. Furthermore, the system achieves a reliability score of 94.86% and a stability measure of 0.82. These findings highlight the potential of quantum computing technologies in transforming healthcare emergency response systems through intelligent and efficient resource management.</p>

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Quantum computing-inspired resource distribution in healthcare

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • Abdullah Alqahtani,
  • Munish Bhatia

摘要

Advancements in the internet of things technology have significantly improved the quality of healthcare services, particularly through the integration of artificial intelligence. However, the healthcare sector continues to face persistent challenges, such as the increasing population, the rising burden of diseases, and the limited availability of critical medical resources. These challenges are especially pronounced during medical emergencies, where the timely and optimal allocation of healthcare resources is vital. To address this pressing issue, the current research explores the use of quantum computing in conjunction with fog computing to optimize resource distribution in emergency scenarios. The study proposes a quantum-based mapping framework that utilizes quantum-inspired techniques to calculate the medical resource availability index for individual hospitals and healthcare centers. This enables real-time prediction and efficient allocation of resources during emergencies. In addition, an intelligent quantum neural network is developed to forecast the future demand for medical resources based on dynamic emergency conditions. To validate the proposed system, a 60-day controlled simulation was conducted involving four healthcare centers. The results demonstrate that the proposed technique outperforms existing state-of-the-art resource allocation methods across multiple performance metrics. It achieves a low temporal delay of 4.59 ms with 2.15% performance gain and exhibits high quantum mapping efficacy with a precision of 96.05%, sensitivity of 96.93%, and specificity of 97.46%. The decision-making component also shows strong performance, with a precision of 92.69%, sensitivity of 91.65%, specificity of 94.26%, and an F-measure of 95.26%, resulting in an average performance gain of 3.25% in comparison to state-of-the-art models. Furthermore, the system achieves a reliability score of 94.86% and a stability measure of 0.82. These findings highlight the potential of quantum computing technologies in transforming healthcare emergency response systems through intelligent and efficient resource management.