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arXiv · 2412.06857

Comb Tensor Networks vs. Matrix Product States: Enhanced Efficiency in High-Dimensional Spaces

Danylo Kolesnyk·Yelyzaveta Vodovozova
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Abstract

Modern approaches to generative modeling of continuous data using tensor networks incorporate compression layers to capture the most meaningful features of high-dimensional inputs. These methods, however, rely on traditional Matrix Product States (MPS) architectures. Here, we demonstrate that beyond a certain threshold in data and bond dimensions, a comb-shaped tensor network architecture can yield more efficient contractions than a standard MPS. This finding suggests that for continuous and high-dimensional data distributions, transitioning from MPS to a comb tensor network representation can substantially reduce computational overhead while maintaining accuracy.

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Danylo Kolesnyk, Yelyzaveta Vodovozova. 2024-12-08. Comb Tensor Networks vs. Matrix Product States: Enhanced Efficiency in High-Dimensional Spaces. https://arxiv.org/abs/2412.06857

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