Quantum Entanglement-Based Distributed Inference

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper proposes a novel distributed inference architecture leveraging the unique properties of quantum entanglement to achieve exponentially faster computation compared to classical networks. The core concept involves establishing a network of spatially distributed quantum processors interconnected via entangled qubits. A query is encoded into a superposition state, which then propagates through the network via quantum entanglement, allowing each processor to perform a specific computation on its portion of the superposition. The resulting correlations are subsequently measured to yield the inference result. This approach represents a significant departure from traditional distributed computing paradigms, directly exploiting quantum entanglement for computation and offering the potential for dramatically improved efficiency and scalability in complex inference tasks. The architecture is designed with scalability in mind, utilizing modular components and efficient entanglement distribution protocols. We outline the theoretical framework, detailing the key mathematical concepts and formulas involved, and discuss the potential challenges and future research directions. The primary focus is on demonstrating the feasibility and performance benefits of this entanglement-based approach, paving the way for a new generation of quantum-enhanced distributed systems.

DOI: 10.5281/zenodo.22184084

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