Quantum-Inspired Optimization of Large-Scale Distributed Algorithms

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of quantum-inspired optimization techniques to accelerate the convergence of large-scale distributed algorithms. Traditional distributed algorithm design often relies on classical optimization methods, which can struggle with the complexity and scale inherent in these systems. We propose a novel approach that borrows concepts from quantum mechanics, specifically amplitude amplification and interference, to guide the search for optimal solutions. The core idea is to utilize superposition and interference effects to explore a broader solution space more efficiently than conventional methods. We formulate a generalized framework for integrating these techniques, focusing on the iterative refinement of algorithm parameters. Preliminary analysis and theoretical considerations suggest that this approach holds promise for significant performance improvements in areas such as sensor network routing, distributed machine learning, and consensus protocols. This research aims to bridge the gap between quantum computing concepts and classical distributed systems, offering a potentially transformative method for optimizing algorithm performance.

DOI: 10.5281/zenodo.22199160

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