Quantum Entanglement-Driven Distributed Causal Inference

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper proposes a novel approach to distributed causal inference leveraging the unique properties of quantum entanglement. Traditional methods of causal inference face challenges in distributed systems, including latency, information silos, and difficulty in establishing robust causal relationships across geographically dispersed data sources. Our framework utilizes multiple quantum sensors to collect data from a distributed system. Quantum entanglement is then employed to correlate the measurement results from these sensors in real-time. This correlation is achieved through quantum entanglement-based quantum teleportation, facilitating the rapid propagation and synchronization of causal information. A causal inference model is constructed based on the characteristics of the entangled states, enabling real-time reasoning. The core claim is that this approach allows for real-time, high-precision inference of causal relationships in distributed systems, effectively addressing the limitations of conventional methods. We demonstrate the potential of exploiting quantum mechanics to achieve efficient causal inference in distributed environments.

DOI: 10.5281/zenodo.22293293

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