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 …
Graph neural networks at the Large Hadron Collider
From raw detector activations to reconstructed particles, data at the Large Hadron Collider
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …
(LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural …
Quantum anomaly detection in the latent space of proton collision events at the LHC
The ongoing quest to discover new phenomena at the LHC necessitates the continuous
development of algorithms and technologies. Established approaches like machine …
development of algorithms and technologies. Established approaches like machine …
Taking advantage of noise in quantum reservoir computing
The biggest challenge that quantum computing and quantum machine learning are currently
facing is the presence of noise in quantum devices. As a result, big efforts have been put into …
facing is the presence of noise in quantum devices. As a result, big efforts have been put into …
Classical versus quantum: Comparing tensor-network-based quantum circuits on Large Hadron Collider data
Tensor networks (TN) are approximations of high-dimensional tensors designed to
represent locally entangled quantum many-body systems efficiently. This paper provides a …
represent locally entangled quantum many-body systems efficiently. This paper provides a …
Financial fraud detection using quantum graph neural networks
Financial fraud detection is essential for preventing significant financial losses and
maintaining the reputation of financial institutions. However, conventional methods of …
maintaining the reputation of financial institutions. However, conventional methods of …
On the design of quantum graph convolutional neural network in the nisq-era and beyond
The rapid growth in the size of Graph Convolutional Neural Networks (GCNs) encounters
both computational-and memory-wall on classical computing platforms (eg, CPU, GPU …
both computational-and memory-wall on classical computing platforms (eg, CPU, GPU …
Quantum computing for data analysis in high energy physics
Some of the biggest achievements of the modern era of particle physics, such as the
discovery of the Higgs boson, have been made possible by the tremendous effort in building …
discovery of the Higgs boson, have been made possible by the tremendous effort in building …
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 …
Snowmass white paper: Quantum computing systems and software for high-energy physics research
Quantum computing offers a new paradigm for advancing high-energy physics research by
enabling novel methods for representing and reasoning about fundamental quantum …
enabling novel methods for representing and reasoning about fundamental quantum …