83 citations · 234 across the 18 of their papers we have counts for
18 papers
Robust Quantum Machine Learning for Collider Event Selection under Detector Variability
Christopher Brown, Michael Spannowsky, Simon Williams
Robust machine-learning methods are becoming increasingly important for high-energy physics data analysis as experiments enter the era of higher luminosity and future higher-energy…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
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-…
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…
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…
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…
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…
The role of data embedding in quantum autoencoders for improved anomaly detection
Jack Y. Araz, Michael Spannowsky
The performance of Quantum Autoencoders (QAEs) in anomaly detection tasks is critically dependent on the choice of data embedding and ansatz design. This study explores the effects…
Enhancing Quantum Field Theory Simulations on NISQ Devices with Hamiltonian Truncation
James Ingoldby, Michael Spannowsky, Timur Sypchenko +1
Quantum computers can efficiently simulate highly entangled quantum systems, offering a solution to challenges facing classical simulation of Quantum Field Theories (QFTs). This pa…
Simulating quantum field theories on continuous-variable quantum computers
Steven Abel, Michael Spannowsky, Simon Williams
We delve into the use of photonic quantum computing to simulate quantum mechanics and extend its application towards quantum field theory. We develop and prove a method that levera…
Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms
Jack Y. Araz, Michael Spannowsky, Matthew Wingate
This study investigates the thermal properties of the repulsive Fermi-Hubbard model with chemical potential using variational quantum algorithms, crucial in comprehending particle…
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…
Simulating Lattice Gauge Theory with the Variational Quantum Thermalizer
Michael Fromm, Owe Philipsen, Michael Spannowsky +1
The properties of strongly-coupled lattice gauge theories at finite density as well as in real time have largely eluded first-principles studies on the lattice. This is due to the…
The CLIC Potential for New Physics
J. de Blas, R. Franceschini, F. Riva +100
The Compact Linear Collider (CLIC) is a mature option for the future of high energy physics. It combines the benefits of the clean environment of colliders with operation…
Resonant Di-Higgs Production in the Channel: Probing the Electroweak Phase Transition at the LHC
T. Huang, J. M. No, L. Pernié +4
We analyze the prospects for resonant di-Higgs production searches at the LHC in the (, ) channel,…