Search Results for author:
Found 11 papers, 6 papers with code
TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era
We have made use of the REDVID simulation framework, as well as reductions applied to the TrackML data set, to compose five data sets from simple to complex, for our experiments.
Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering
This research sheds light on previously unexplored methods and provides valuable insights for the field of particle track reconstruction and hit clustering in HEP.
Machine Learning for maximizing the memristivity of single and coupled quantum memristors
We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors.
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation
We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with $N_{Q}$ qubits.
Active Learning in Physics: From 101, to Progress, and Perspective
Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence.
Deep Learning for fully automatic detection, segmentation, and Gleason Grade estimation of prostate cancer in multiparametric Magnetic Resonance Images
The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of patients for confirmation biopsy.
Towards Pricing Financial Derivatives with an IBM Quantum Computer
Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates.
Quantum Physics Mesoscale and Nanoscale Physics
Machine learning method for single trajectory characterization
In this paper, we propose a machine learning method based on a random forest architecture, which is able to associate even very short trajectories to the underlying diffusion mechanism with a high accuracy.
A Probabilistic framework for Quantum Clustering
However, it is very sensitive to a length parameter that is inherent to the Schr\"odinger equation.
Supervised Quantum Learning without Measurements
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations.
Optimization of anemia treatment in hemodialysis patients via reinforcement learning
Results: The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level.
