Quantum Entanglement-Accelerated Probability Inference

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the potential for accelerating probability inference using quantum entanglement. We propose a novel approach where probability models are mapped onto entangled quantum bit (qubit) systems, enabling parallel exploration of all possible probabilistic branches. The core claim is that this entanglement-assisted method can significantly reduce the computational time required for complex probabilistic models, particularly those with a large number of possible outcomes. The mechanism leverages the inherent correlations within entangled qubits to perform parallel computations. We introduce a theoretical framework outlining this process and discuss the potential advantages over classical sequential methods. The exploration of this area promises to revolutionize probabilistic computing, opening new avenues for tackling computationally intensive problems in fields like machine learning, statistical physics, and decision theory. The efficiency gains stem from the ability to simultaneously evaluate multiple probability branches, a capability fundamentally unavailable to classical computers. The ultimate goal is to demonstrate a quantifiable speedup in inference processes.

DOI: 10.5281/zenodo.22202581

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