Quantum Entanglement-Based Distributed Multi-Task Parallel Inference

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel approach to multi-task parallel inference leveraging the unique properties of quantum entanglement. The core claim is that utilizing entanglement enables asymmetric information transfer and collaborative processing between distributed computing nodes, thereby overcoming limitations inherent in traditional distributed computing architectures and dramatically increasing efficiency and the ability to handle complex tasks. The proposed mechanism involves constructing a distributed network comprised of multiple quantum processors, each dedicated to processing a subset of tasks. Through carefully designed entanglement operations, data across these processors are entangled, facilitating simultaneous processing by multiple nodes. Quantum measurement and entanglement decoupling techniques are then employed to consolidate the results, ultimately yielding the final outcome. This approach represents a significant departure from classical distributed computing, which relies on bandwidth-constrained and latency-prone communication channels. By exploiting entanglement, the proposed system bypasses these limitations, unlocking the potential for true parallel quantum computation and significantly enhancing the efficiency and complexity handling capabilities of multi-task parallel inference. ---

DOI: 10.5281/zenodo.22198685

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