Quantum-Inspired Consensus Algorithms for Distributed Machine Learning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper introduces a novel family of consensus algorithms inspired by quantum mechanics, termed Quantum-Inspired Consensus Algorithms (QICA). Traditional consensus algorithms, prevalent in distributed machine learning, suffer from vulnerabilities to adversarial attacks and inherent limitations in scalability due to reliance on majority voting. QICA addresses these shortcomings by incorporating principles of quantum superposition and interference. Each node maintains a probabilistic representation of the solution, and interference patterns are strategically utilized to amplify the probability of correct solutions while simultaneously suppressing incorrect ones. This approach eliminates the need for explicit majority voting, resulting in a more robust and scalable consensus process. The core mechanism relies on a carefully designed update rule that leverages these quantum-inspired concepts. The algorithm's performance is evaluated theoretically, demonstrating its potential to achieve consensus with significantly reduced communication overhead and increased resistance to malicious nodes. Key performance metrics, such as convergence rate and resilience to attacks, are analyzed. The proposed methodology offers a promising avenue for enhancing the security and efficiency of distributed learning systems.

DOI: 10.5281/zenodo.22242696

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