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Quantum-Enhanced Fractional Modeling of Zombie Infection Dynamics Using Hybrid Quantum Algorithms |
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PP: 105-113 |
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doi:10.18576/pfda/120107
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Author(s) |
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Feras Fares AL-Mashakbah,
Srinivasarao Thota,
Ala’a Saeb Al-Sherideh,
Mohammad Rasmi Al-Mousa,
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Abstract |
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| This article proposes a hybrid quantum-classical approach to model the dynamic evolution of infectious diseases via a fractional-order SIZR (Susceptible-Infected-Zombie-Removed) model. By leveraging a classical model improved by an Atangana-Baleanu fractional derivative approach, memory terms are introduced, which play an essential role in achieving realistic epidemic evolution results. The proposed methodology involves reformulating the epidemic evolution model via a discretized approach to fractional derivatives and applying it to a quantum circuit via amplitude encoding. Additionally, via quantum-classical optimization, a parameterized quantum ansatz is optimized in an attempt to minimize a cost function that considers the residuals associated with the fractional model. Numerical experiments carried out via IBM’s Qiskit Aer simulator have enabled us to detect complex, high memory terms associated with all four compartments in a zombie infection model in an epidemic evolution system. The proposed approach confirms efficacy based on comparative results for varying fractional parameters α, denoting improved performance in computational efficiency and scalability when contrasted with classical approaches such as HATM. This article provides groundwork for forthcoming uses related to quantum computing applications in epidemiology, specifically in fractional dynamic systems.
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