Quantum-Inspired Distributed Memory Networks

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper presents a novel architecture for distributed memory networks inspired by the principles of quantum superposition and entanglement. Traditional distributed memory systems struggle with scalability and efficiency when dealing with large datasets and complex pattern recognition tasks. Our approach addresses these limitations by leveraging probabilistic representations of data nodes, mimicking quantum amplitudes, which enable multiple simultaneous representations and correlations. A key component is the "entanglement protocol," designed to facilitate efficient information sharing and accelerate retrieval processes across the network. The core claim of this work is to demonstrate exponentially faster information retrieval and pattern recognition capabilities compared to conventional distributed memory models. We outline the theoretical framework, detailing the mathematical formulations underlying the network architecture and the entanglement protocol. The proposed system offers a fundamentally different paradigm for data storage and access, moving beyond classical constraints and potentially unlocking significant performance gains in data-intensive applications.

DOI: 10.5281/zenodo.22248944

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