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20172026
most citedThe CLIC Potential for New Physics

83 citations · 234 across the 18 of their papers we have counts for

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Showing 2025Show all

8 papers · 1 filter

hep-ph2025

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…

hep-ph2025

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-…

quant-ph2025★ 1 cited

Continuous-variable photonic quantum extreme learning machines for fast collider-data selection

Benedikt Maier, Michael Spannowsky, Simon Williams

We study continuous-variable photonic quantum extreme learning machines as fast, low-overhead front-ends for collider data processing. Data is encoded in photonic modes through qua…

quant-ph2025

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…

quant-ph2025

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…

hep-ph2025★ 1 cited

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…

hep-ph2025★ 3 cited

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…

quant-ph2025★ 9 cited

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…