1 citations · 2 across the 5 of their papers we have counts for
6 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…
Riemannian Optimization on Tree Tensor Networks with Application in Machine Learning
Marius Willner, Marco Trenti, Dirk Lebiedz
Tree tensor networks (TTNs) are widely used in low-rank approximation and quantum many-body simulation. In this work, we present a formal analysis of the quotient geometry underlyi…
QuantumDNA: A Python Package for Analyzing Quantum Charge Dynamics in DNA and Exploring Its Biological Relevance
Dennis Herb, Marco Trenti, Marilena Mantela +3
The study of DNA charge dynamics is a highly interdisciplinary field that bridges physics, chemistry, biology, and medicine, and plays a critical role in processes such as DNA dama…
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA
Lorenzo Borella, Alberto Coppi, Jacopo Pazzini +4
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning…
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…