Quantum computing for high-energy physics: State of the art and challenges
Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …
natural sciences and beyond, with the potential for achieving a so-called quantum …
[HTML][HTML] Machine learning for anomaly detection in particle physics
V Belis, P Odagiu, TK Aarrestad - Reviews in Physics, 2024 - Elsevier
The detection of out-of-distribution data points is a common task in particle physics. It is used
for monitoring complex particle detectors or for identifying rare and unexpected events that …
for monitoring complex particle detectors or for identifying rare and unexpected events that …
Quantum anomaly detection in the latent space of proton collision events at the LHC
We propose a new strategy for anomaly detection at the LHC based on unsupervised
quantum machine learning algorithms. To accommodate the constraints on the problem size …
quantum machine learning algorithms. To accommodate the constraints on the problem size …
Guided quantum compression for high dimensional data classification
Quantum machine learning provides a fundamentally different approach to analyzing data.
However, many interesting datasets are too complex for currently available quantum …
However, many interesting datasets are too complex for currently available quantum …
Improving new physics searches with diffusion models for event observables and jet constituents
A bstract We introduce a new technique called D rapes to enhance the sensitivity in
searches for new physics at the LHC. By training diffusion models on side-band data, we …
searches for new physics at the LHC. By training diffusion models on side-band data, we …
A quantum computing implementation of nuclearelectronic orbital (NEO) theory: Toward an exact pre-Born–Oppenheimer formulation of molecular quantum systems
Nuclear quantum phenomena beyond the Born–Oppenheimer approximation are known to
play an important role in a growing number of chemical and biological processes. While …
play an important role in a growing number of chemical and biological processes. While …
Quantum generative adversarial networks for anomaly detection in high energy physics
The standard model (SM) of particle physics represents a theoretical paradigm for the
description of the fundamental forces of nature. Despite its broad applicability, the SM does …
description of the fundamental forces of nature. Despite its broad applicability, the SM does …
Anomaly detection in collider physics via factorized observables
EM Metodiev, J Thaler, R Wynne - Physical Review D, 2024 - APS
To maximize the discovery potential of high-energy colliders, experimental searches should
be sensitive to unforeseen new physics scenarios. This goal has motivated the use of …
be sensitive to unforeseen new physics scenarios. This goal has motivated the use of …
Enforcing exact permutation and rotational symmetries in the application of quantum neural networks on point cloud datasets
Z Li, L Nagano, K Terashi - Physical Review Research, 2024 - APS
Recent developments in the field of quantum machine learning have promoted the idea of
incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in …
incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in …
Unsupervised and lightly supervised learning in particle physics
We review the main applications of machine learning models that are not fully supervised in
particle physics, ie, clustering, anomaly detection, detector simulation, and unfolding …
particle physics, ie, clustering, anomaly detection, detector simulation, and unfolding …