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Found 5 papers, 2 papers with code
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks.
$\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples.
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
In this paper, we perform a fair and comprehensive comparison between classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN).
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
It is shown that the model selection with distributed inference HALP can significantly improve service reliability compared to the conventional stand-alone computation.
Is the Machine Smarter than the Theorist: Deriving Formulas for Particle Kinematics with Symbolic Regression
We demonstrate the use of symbolic regression in deriving analytical formulas, which are needed at various stages of a typical experimental analysis in collider phenomenology.
