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Found 5 papers, 4 papers with code
Event Classification with Multi-step Machine Learning
The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented.
Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications
The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware co-optimization.
Quantum Physics
An Improvement of Object Detection Performance using Multi-step Machine Learnings
Connecting multiple machine learning models into a pipeline is effective for handling complex problems.
Event Classification with Quantum Machine Learning in High-Energy Physics
We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics.
Computational Physics High Energy Physics - Experiment High Energy Physics - Phenomenology Quantum Physics
LHC Dark Matter Working Group: Next-generation spin-0 dark matter models
The exclusion limits obtained from the proposed scans can be consistently compared to the constraints on the $\textrm{2HDM+a}$ model that derive from DD, ID and the DM relic density.
High Energy Physics - Experiment High Energy Physics - Phenomenology
