79 citations · 514 across the 32 of their papers we have counts for
6 papers · 1 filter
Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection
Jack Y. Araz, Michael Spannowsky
The Hamiltonian of an isolated quantum mechanical system determines its dynamics and physical behaviour. This study investigates the possibility of learning and utilising a system'…
Towards a Quantum Simulation of Nonlinear Sigma Models with a Topological Term
Jack Y. Araz, Sebastian Schenk, Michael Spannowsky
We determine the mass gap of a two-dimensional nonlinear sigma model augmented with a topological -term using tensor network and digital quantum algorithms. As proof of p…
Strength in numbers: optimal and scalable combination of LHC new-physics searches
Jack Y. Araz, Andy Buckley, Benjamin Fuks +4
To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in gen…
Signal region combination with full and simplified likelihoods in MadAnalysis 5
Gaël Alguero, Jack Y. Araz, Benjamin Fuks +1
The statistical combination of disjoint signal regions in reinterpretation studies uses more of the data of an analysis and gives more robust results than the single signal region…
Identifying magnetic antiskyrmions while they form with convolutional neural networks
Jack Y. Araz, Juan Carlos Criado, Michael Spannowsky
Chiral magnets have attracted a large amount of research interest in recent years because they support a variety of topological defects, such as skyrmions and bimerons, and allow f…
Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
Jack Y. Araz, Michael Spannowsky
Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. This study provides a comprehensi…