7 papers
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-…
Qumode Tensor Networks for False Vacuum Decay in Quantum Field Theory
Steven Abel, Michael Spannowsky, Simon Williams
False vacuum decay in scalar quantum field theory (QFT) is a cornerstone of early Universe cosmology and high energy physics, yet its real-time dynamics is essentially inaccessible…
Real-Time Scattering on Quantum Computers via Hamiltonian Truncation
James Ingoldby, Michael Spannowsky, Timur Sypchenko +2
We present a quantum computational framework using Hamiltonian Truncation (HT) for simulating real-time scattering processes in -dimensional scalar theory. Unlike trad…
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…
Real-Time Scattering Processes with Continuous-Variable Quantum Computers
Steven Abel, Michael Spannowsky, Simon Williams
We propose a framework for simulating the real-time dynamics of quantum field theories (QFTs) using continuous-variable quantum computing (CVQC). Focusing on ()-dimensional $φ…