Distributed Representation of Uncertainty with Tensor Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel approach to representing and quantifying uncertainty in complex systems using tensor networks. Traditional methods often struggle with the intricate correlations inherent in these systems, leading to inaccurate or computationally expensive uncertainty estimates. We propose leveraging the power of tensor networks to directly represent the joint distribution of variables, capturing these correlations efficiently. This approach offers the potential for significantly improved accuracy and scalability compared to conventional probabilistic representations. The core idea is to encode the uncertainty as a distributed representation within a tensor network, enabling efficient computation of correlations and ultimately, more precise uncertainty quantification. We demonstrate the conceptual framework and outline the key elements for developing a practical implementation of this method.

DOI: 10.5281/zenodo.22203078

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