80 citations · 209 across the 7 of their papers we have counts for
8 papers
Stochastic Optimization of Tree Tensor Networks
Marius Willner, Maximilian Scharf, André Uschmajew +2
Tensor networks, originally developed for quantum many-body physics, are promising models for machine learning. We derive stochastic Riemannian optimizers for tree tensor networks…
Quantum-Inspired Robust and Scalable SAR Object Classification
Maximilian Scharf, Marco Trenti, Felix Bock +5
SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additionally, the deployment of these m…
Efficient Tensor Network ansatz for high-dimensional quantum many-body problems
Timo Felser, Simone Notarnicola, Simone Montangero
We introduce a novel tensor network structure augmenting the well-established Tree Tensor Network representation of a quantum many-body wave function. The new structure satisfies t…
Lattice Quantum Electrodynamics in (3+1)-dimensions at finite density with Tensor Networks
Giuseppe Magnifico, Timo Felser, Pietro Silvi +1
Gauge theories are of paramount importance in our understanding of fundamental constituents of matter and their interactions. However, the complete characterization of their phase…
Optimizing Radiotherapy Plans for Cancer Treatment with Tensor Networks
Samuele Cavinato, Timo Felser, Marco Fusella +2
We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the Intensit…
Quantum-inspired Machine Learning on high-energy physics data
Timo Felser, Marco Trenti, Lorenzo Sestini +4
Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree ten…
Two-dimensional quantum-link lattice Quantum Electrodynamics at finite density
Timo Felser, Pietro Silvi, Mario Collura +1
We present an unconstrained tree tensor network approach to the study of lattice gauge theories in two spatial dimensions showing how to perform numerical simulations of theories i…
On the descriptive power of Neural-Networks as constrained Tensor Networks with exponentially large bond dimension
Mario Collura, Luca Dell'Anna, Timo Felser +1
In many cases, Neural networks can be mapped into tensor networks with an exponentially large bond dimension. Here, we compare different sub-classes of neural network states, with…