The first measurement of lepton-jet momentum imbalance and azimuthal correlation in
lepton-proton scattering at high momentum transfer is presented. These data, taken with the …

The task of learning a probability distribution from samples is ubiquitous across the natural
sciences. The output distributions of local quantum circuits are of central importance in both …

Hadronization is a complex quantum process whereby quarks and gluons become hadrons.
The widely used models of hadronization in event generators are based on physically …

We introduce a new class of iterative image reconstruction algorithms for radio
interferometry, at the interface of convex optimization and deep learning, inspired by plug …

Unfolding is an important procedure in particle physics experiments that corrects for detector
effects and provides differential cross section measurements that can be used for a number …

We study the consequences of mode-collapse of normalizing flows in the context of lattice
field theory. Normalizing flows allow for independent sampling. For this reason, it is hoped …

CityCOVID is a detailed agent-based model that represents the behaviors and social
interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 …

Accurately estimating personalized treatment effects within a study site (eg, a hospital) has
been challenging due to limited sample size. Furthermore, privacy considerations and lack …

Abstract Deep Gradient Leakage (DGL) is a highly effective attack that recovers private
training images from gradient vectors. This attack casts significant privacy challenges on …

We describe a quantum-assisted machine learning method in which multivariate data are
encoded into quantum states in a Hilbert space whose dimension is exponentially large in …