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arXiv · 2602.22372

Adaptive Patching for Tensor Train Computations

Gianluca Grosso·Marc K. Ritter·Stefan Rohshap·Samuel Badr·Anna Kauch·Markus Wallerberger·Jan von Delft·Hiroshi Shinaoka
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Abstract

Quantics Tensor Train (QTT) operations such as matrix product operator contractions are prohibitively expensive for large bond dimensions. We propose an adaptive patching scheme that exploits block-sparse QTT structures to reduce costs through divide-and-conquer, adaptively partitioning tensors into smaller patches with reduced bond dimensions. We demonstrate substantial improvements for sharply localized functions and show efficient computation of bubble diagrams and Bethe-Salpeter equations, opening the door to practical large-scale QTT-based computations previously beyond reach.

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Gianluca Grosso, Marc K. Ritter, Stefan Rohshap, Samuel Badr, Anna Kauch, Markus Wallerberger, Jan von Delft, Hiroshi Shinaoka. 2026-02-25. Adaptive Patching for Tensor Train Computations. https://arxiv.org/abs/2602.22372

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