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Trapped Ion Quantum Computing

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

arXiv
Authors: Danylo Kolesnyk, Yelyzaveta Vodovozova

Year

2024

Paper ID

6248

Status

Preprint

Abstract Read

~2 min

Abstract Words

88

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • Modern approaches to generative modeling of continuous data using tensor networks incorporate compression layers to capture the most meaningful features of high-dimensional inputs.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2412.06857 [2] arXiv https://arxiv.org/abs/2412.06857 [3] Publisher https://arxiv.org/abs/2412.06857

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