Quantum Topology for Probabilistic Machine Learning

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum topology, a field of physics and mathematics, describes geometric structures that exhibit unusual properties of entanglement and non-locality. This paper explores the application of quantum topology to probabilistic machine learning, focusing on the design of a model leveraging quantum spin states and rotations to learn data distributions and predict outcomes. We propose a novel approach that utilizes the inherent quantum properties of spacetime to create a probabilistic model capable of enhanced prediction and classification. The core mechanism involves mapping data into a quantum state, allowing the model to exploit entanglement and superposition to achieve superior performance. This research addresses the limitations of traditional machine learning algorithms by introducing a fundamentally different approach based on quantum principles. The benefits include improved robustness, adaptability, and the potential for uncovering hidden patterns within complex data.

DOI: 10.5281/zenodo.22248603

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