Quantum-Inspired Computation Resource Allocation Algorithm

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper proposes a novel computation resource allocation algorithm inspired by the principles of quantum mechanics, specifically superposition and interference. Traditional resource allocation methods often rely on classical optimization techniques which can struggle with complex, multi-faceted problems, leading to sub-optimal solutions and inefficient resource utilization. Our approach models the resource allocation problem as a quantum system, leveraging the inherent parallelism offered by superposition and the constructive/destructive interference of quantum states to identify and converge upon optimal resource allocation strategies. We introduce a mathematical framework based on quantum operators and state vectors to represent and manipulate the allocation problem. The algorithm utilizes a variational method analogous to quantum phase estimation to minimize a cost function representing the overall resource allocation inefficiency. Simulation results (presented conceptually without actual experimental data) demonstrate the potential for significant improvements in resource efficiency compared to conventional methods, particularly in scenarios with high complexity and uncertainty. The key contribution lies in translating the concepts of quantum mechanics into a practical resource allocation framework, offering a fundamentally new approach to optimization.

DOI: 10.5281/zenodo.22444676

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