5 papers · 1 filter
Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics
Jack Y. Araz, Michael Spannowsky
Machine-learning techniques are essential in modern collider research, yet their probabilistic outputs often lack calibrated uncertainty estimates and finite-sample guarantees, lim…
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…
Quantum Pathways for Charged Track Finding in High-Energy Collisions
Christopher Brown, Michael Spannowsky, Alexander Tapper +2
In high-energy particle collisions, charged track finding is a complex yet crucial endeavour. We propose a quantum algorithm, specifically quantum template matching, to enhance the…
Communicating Likelihoods with Normalising Flows
Jack Y. Araz, Anja Beck, Méril Reboud +2
We present a machine-learning-based workflow to model an unbinned likelihood from its samples. A key advancement over existing approaches is the validation of the learned likelihoo…