Quantum computing algorithm for optimizing query distribution based on multi-criteria parameters in information systems
Abstract
Optimal query distribution in information systems plays a crucial role in minimizing delays, balancing workload, and ensuring system stability. Considering multi-factor parameters such as server capacity, network latency, and service rate, the fuzzy logic approach proves effective in handling uncertainty during decision-making. However, an increase in the number of linguistic variables leads to exponential growth of the fuzzy rule base, thereby increasing computational complexity and limiting real-time performance. Traditional optimization methods, including genetic algorithms, are constrained by slow convergence and a tendency to fall into local extrema in large search spaces. This paper proposes a multi-objective quantum optimization approach aimed at reducing the number of fuzzy rules in query distribution processes. By applying Grover's quantum algorithm, the search for optimal rules is significantly accelerated, and the search space is significantly reduced. Experimental results demonstrate reduced response time and improved load balancing. The proposed approach is evaluated as a promising quantum-intelligent mechanism for enhancing load management efficiency in cloud computing environments.