Work

Quantum Tensor Network Learning with DMRG

Year: 2025

Type: conference-paper

Abstract: Tensor Networks are a relatively new machine learning approach.The architectures proposed initially are inspired by approaches from quantum many-body physics simulations.One common layout is the matrix product state (MPS) also known as a tensor train optimized with gradient descent techniques.We introduce a global normalization condition, so that the MPS represents a quantum state.We investigate two optimization methods that find the locally optimal tensors and compare them regarding their effectiveness.One is based on gradient descent and the other on an adaptation of DMRG.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-157

Authors: Gustav Jäger, Martin B. Plenio, Hans-Martin Rieser

Institutions: Universität Ulm, Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Language: English

Topic: Quantum many-body systems

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409481101

FWCI: 7.398

Cites:

Cited by: 1

Related to: 10