research
∙
08/24/2023
Training Neural Networks with Universal Adiabatic Quantum Computing
The training of neural networks (NNs) is a computationally intensive tas...
share
research
∙
02/24/2023
Generative Invertible Quantum Neural Networks
Invertible Neural Networks (INN) have become established tools for the s...
research
∙
11/07/2022
Quantum-probabilistic Hamiltonian learning for generative modelling anomaly detection
The Hamiltonian of an isolated quantum mechanical system determines its ...
research
∙
09/15/2022
A Genetic Quantum Annealing Algorithm
A genetic algorithm (GA) is a search-based optimization technique based ...
research
∙
05/23/2022
Identifying (anti-)skyrmions while they form
We use a Convolutional Neural Network (CNN) to identify the relevant fea...
research
∙
04/26/2022
IRC-safe Graph Autoencoder for an unsupervised anomaly detection
Anomaly detection through employing machine learning techniques has emer...
research
∙
04/07/2022
Qade: Solving Differential Equations on Quantum Annealers
We present a general method, called Qade, for solving differential equat...
research
∙
02/23/2022
Completely Quantum Neural Networks
Artificial neural networks are at the heart of modern deep learning algo...
research
∙
02/22/2022
A duality connecting neural network and cosmological dynamics
We demonstrate that the dynamics of neural networks trained with gradien...
research
∙
02/21/2022
Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
Tensor Networks (TN) are approximations of high-dimensional tensors desi...
research
∙
06/15/2021
Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States
Tensor Networks are non-trivial representations of high-dimensional tens...
research
∙
05/28/2021
Quantum Optimisation of Complex Systems with a Quantum Annealer
We perform an in-depth comparison of quantum annealing with several clas...
research
∙
03/26/2021
