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 …

A quantum algorithm is proposed for sampling from a solution of stochastic differential
equations (SDEs). Using differentiable quantum circuits (DQCs) with a feature map …

Anomaly detection in Endpoint Detection and Response (EDR) is a critical task in
cybersecurity programs of large companies. With rapidly growing amounts of data and the …

This work investigates in detail the performance and advantages of a new quantum Monte
Carlo integrator, dubbed quantum Fourier iterative amplitude estimation (QFIAE), to …

This Letter investigates in detail the performance and advantages of a new quantum Monte
Carlo integrator, dubbed Quantum Fourier Iterative Amplitude Estimation (QFIAE), to …

Geometric quantum machine learning (GQML) aims to embed problem symmetries for
learning efficient solving protocols. However, the question remains if (G) QML can be …

In generative learning, models are trained to produce new samples that follow the
distribution of the target data. These models were historically difficult to train, until proposals …

We propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful
energy-based neural network. It can be realized experimentally on a continuous-variable …

We present the first quantum computation of a total decay rate in high-energy physics at
second order in perturbative quantum field theory. This work underscores the confluence of …

Quantum devices are being developed to perform computation in an inherently non-
classical way. These devices are fundamentally different from conventional computers and …