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Found 7 papers, 1 papers with code
Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactions
Through the integration of classical computation and quantum simulation, this hybrid framework paves way for utilizing large-scale quantum simulations as priors in deep generative models.
Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
We further propose a novel adaptive mapping to estimate the effective inverse temperature in quantum annealers.
CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models
The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events.
CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation
Calorimeter simulation is the most computationally expensive part of Monte Carlo generation of samples necessary for analysis of experimental data at the Large Hadron Collider (LHC).
Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors
Matter-antimatter asymmetry is one of the major unsolved problems in physics that can be probed through precision measurements of charge-parity symmetry violation at current and next-generation neutrino oscillation experiments.
Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage
Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports.
Collective Learning From Diverse Datasets for Entity Typing in the Wild
The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets.
