Normalized tensor train decomposition

TL;DR

This paper introduces the normalized tensor train (NTT) decomposition, a low-rank tensor format that enforces unit-norm constraints, with applications in tensor recovery, quantum physics, and high-dimensional eigenvalue problems, supported by geometric analysis and numerical experiments.

Contribution

The paper proposes the NTT decomposition that intrinsically enforces unit-norm constraints, extending tensor train methods with a geometric framework and efficient algorithms for high-dimensional problems.

Findings

NTT tensors form a smooth manifold with Riemannian geometry.

NTT-based methods outperform existing approaches in efficiency and scalability.

Numerical experiments validate the effectiveness of NTT in various applications.

Abstract

Tensors with unit Frobenius norm are fundamental objects in many fields, including scientific computing and quantum physics, which are able to represent normalized eigenvectors and pure quantum states. While the tensor train decomposition provides a powerful low-rank format for tackling high-dimensional problems, it does not intrinsically enforce the unit-norm constraint. To address this, we introduce the normalized tensor train (NTT) decomposition, which aims to approximate a tensor by unit-norm tensors in tensor train format. The low-rank structure of NTT decomposition not only saves storage and computational cost but also preserves the underlying unit-norm structure. We prove that the set of fixed-rank NTT tensors forms a smooth manifold, and the corresponding Riemannian geometry is derived, paving the way for geometric methods. We propose NTT-based methods for low-rank tensor recovery, high-dimensional eigenvalue problem, estimation of stabilizer rank, and calculation of the minimum output R\'enyi 2-entropy of quantum channels. Numerical experiments demonstrate the superior efficiency and scalability of the proposed NTT-based methods.