5 papers
QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models
Aritra Bal, Michael Binder, Markus Klute +2
Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example. We introduce QUIVER (QUantum-Informed Views for En…
From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphs
Aritra Bal, Markus Klute, Benedikt Maier +1
Pairwise Fisher graphs capture local covariance information, but they cannot distinguish an irreducible multi-observable radiation pattern from a collection of ordinary pairwise co…
QINNs: Quantum-Informed Neural Networks
Aritra Bal, Markus Klute, Benedikt Maier +3
Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-…
1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning
Aritra Bal, Markus Klute, Benedikt Maier +3
We introduce 1P1Q, a novel quantum data encoding scheme for high-energy physics (HEP), where each particle is assigned to an individual qubit, enabling direct representation of col…
Distilling particle knowledge for fast reconstruction at high-energy physics experiments
Aritra Bal, Tristan Brandes, Fabio Iemmi +4
Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. Its main application is the compactificat…