The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …

In these proceedings, we review recent advances in applying quantum computing to lattice
field theory. Quantum computing offers the prospect to simulate lattice field theories in …

We review two algorithmic advances that bring us closer to reliable quantum simulations of
model systems in high-energy physics and beyond on noisy intermediate-scale quantum …

We present an error mitigation scheme which corrects readout errors on Noisy Intermediate-
Scale Quantum (NISQ) computers [1, 2]. After a short review of applying the method to one …

Overparametrization is one of the most surprising and notorious phenomena in machine
learning. Recently, there have been several efforts to study if, and how, quantum neural …

The design of parametric quantum circuits (PQCs) for efficient use in variational quantum
simulations (VQS) is subject to two competing factors. On one hand, the set of states that can …

Using tensor network (TN) techniques, we study the Hubbard model on a honeycomb lattice
with a chemical potential, which models the electron structure of graphene. In contrast to …

Las ultimas décadas han evidenciado una rotunda transformación entorno a la mecánica
cuántica, pasando de ser una mera descripción de la naturaleza a liderar una nueva …

In recent years, quantum computing has started to become a very active area of research in
lattice field theory. The reason is that quantum computations offer the exciting possibility to …