2 code implementations • 13 Oct 2021 • Riccardo Di Sipio, Jia-Hong Huang, Samuel Yen-Chi Chen, Stefano Mangini, Marcel Worring
In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing.
1 code implementation • 3 Sep 2019 • Fardin Syed, Riccardo Di Sipio, Pekka Sinervo
A probabilistic reconstruction using machine-learning of the decay kinematics of top-quark pairs produced in high-energy proton-proton collisions is presented.
High Energy Physics - Experiment Computational Physics Data Analysis, Statistics and Probability
2 code implementations • 22 Aug 2019 • Kyle Cormier, Riccardo Di Sipio, Peter Wittek
High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations.
Data Analysis, Statistics and Probability High Energy Physics - Experiment Quantum Physics
1 code implementation • 23 May 2019 • Riccardo Di Sipio
The Parton-Shower algorithm implement in the Pythia generator is applied multiple times to the same parton-level configuration to estimate the systematic uncertainty affecting large-radius jet substructure variables associated with the stochastic nature of the algorithm.
High Energy Physics - Experiment High Energy Physics - Phenomenology
1 code implementation • 6 Mar 2019 • Riccardo Di Sipio, Michele Faucci Giannelli, Sana Ketabchi Haghighat, Serena Palazzo
A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC.
High Energy Physics - Experiment High Energy Physics - Phenomenology
1 code implementation • 6 Aug 2018 • Riccardo Di Sipio
A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.
High Energy Physics - Experiment Data Analysis, Statistics and Probability