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 …

Electric power systems provide the backbone of modern industrial societies. Enabling
scalable grid analytics is the keystone to successfully operating large transmission and …

Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …

Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …

Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …

Recent advances in quantum computing have drawn considerable attention to building
realistic application for and using quantum computers. However, designing a suitable …

Abstract The Large Hadron Collider (LHC) at the European Organisation for Nuclear
Research (CERN) will be upgraded to further increase the instantaneous rate of particle …

Quantum machine learning (QML) can complement the growing trend of using learned
models for a myriad of classification tasks, from image recognition to natural speech …

Much hope for finding new physics phenomena at microscopic scale relies on the
observations obtained from High Energy Physics experiments, like the ones performed at …

We investigate quantum circuits for graph representation learning, and propose equivariant
quantum graph circuits (EQGCs), as a class of parameterized quantum circuits with strong …