← Back to all news
arXiv Machine Learning By Gustav J L J\"ager, Martin B Plenio, Hans-Martin Rieser

Quantum Tensor Network Learning with DMRG

Read the original on arXiv Machine Learning →

The paper introduces a quantum tensor network learning framework that employs matrix product states (MPS) as a machine‑learning architecture, adding a global normalization condition to interpret the MPS as a quantum state. It compares two optimization strategies—gradient descent and a DMRG‑adapted method—to identify locally optimal tensors and evaluates their effectiveness.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Computer Vision
Sep 2

Stochastic Optimization of Tree Tensor Networks

arXiv:2609.00870v1 Announce Type: cross Abstract: Tensor networks, originally developed for quantum many-body physics, are promising models for machine learning. We derive stochastic Riemannian optim...

By Marius Willner, Maximilian Scharf, Andr\'e Uschmajew, Timo Felser, Marco Trenti
More like this →
arXiv Machine Learning
Oct 23

Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding

The paper introduces Tensor Network-based Quantum Data Encoding (TNQE), a framework that uses structured unitary tensor network representations to encode classical data into quantum states efficiently. TNQE decomposes each input into tensor cores and compiles them into encoding circuits via two core-to-circuit strategies, with learnable block unitaries ensuring trainability while maintaining unitarity. The method achieves significantly shallower circuits—down to 0.04× the depth of amplitude encoding—and scales to high‑resolution images, demonstrating practical feasibility on real quantum hardware.

By Guang Lin, Toshihisa Tanaka, Qibin Zhao
benchmarks
More like this →
arXiv Machine Learning
Sep 23

Riemannian Optimization on Tree Tensor Networks with Application in Machine Learning

The paper presents a formal analysis of the quotient geometry of tree tensor networks (TTNs) and introduces efficient first- and second-order optimization algorithms that leverage this geometry. It also develops a backpropagation method for training TTNs in a kernel learning context. Numerical experiments on a digit classification task demonstrate a tradeoff between two horizontal distributions: one provides clearer geometric insights, while the other yields more efficient algorithms.

By Marius Willner, Marco Trenti, Dirk Lebiedz
More like this →