International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)

Dynamic Resource Allocation Using Quantum Algorithm and Game Theoretic Approach

Anjan Bandyopadhyay, Priyanshi Kumari, Debolina Ghosh, Mahendra Kumar Gourisaria, Parthasarathi Pattnayak, Sayan Ghosh

Published Dec 20, 2025• 1 citation

Abstract

The assignment of resources dynamically is a decisive issue in the new paradigms of computations including cloud, edge, fog computing, and the Internet of Things (IoT). These are heterogeneous resource environments, decentralized control environments, and overload environments that vary at a rapid pace and require adaptive and intelligent allocation schemes. The paper suggests a hybrid quantum-game-theoretic model, which combines quantum optimization approaches and multi-agent game theory to solve the problems of multi-agent resource allocation in dynamic systems. Using the quantum concept of superposition and entanglement, the framework can explore the solution spaces in parallel and therefore increase convergence speed and allocation efficiency.A quantum game-theoretic model is developed, which entails price-motivated system to control the strategic interaction of rational agents, in addition to maintaining stability, autonomy and equilibrium. The resource allocation problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solved with the Quantum Approximate Optimization Algorithm (QAOA) algorithm. The proposed approach is proved to be useful as simulation results show significant gains to classical baselines in terms of utility gain, fairness, and system throughput. The results highlight the possibilities of the quantum-game-theoretic paradigm as a fundamental engine of next-generation computing infrastructures, including networked cloud, edge and IoT with networked computing nodes in VPN-enabled networks.

DOI: 10.1109/icrcicn68210.2025.11364802

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