We extend the concept of transfer learning, widely applied in modern machine learning
algorithms, to the emerging context of hybrid neural networks composed of classical and …

Neural quantum states (NQS) attract a lot of attention due to their potential to serve as a very
expressive variational ansatz for quantum many-body systems. Here we study the main …

Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …

Deep Neural Networks have offered numerous innovative solutions to brain-related
diseases including Alzheimer's. However, there are still a few standpoints in terms of …

We introduce a family of neural quantum states for the simulation of strongly interacting
systems in the presence of spatial periodicity. Our variational state is parametrized in terms …

One of the areas with the potential to be explored in quantum computing (QC) is machine
learning (ML), giving rise to quantum machine learning (QML). In an era when there is so …

Inspired by the advancements in large language models based on transformers, we
introduce the transformer quantum state (TQS): a versatile machine learning model for …

We demonstrate, by means of a convolutional neural network, that the features learned in
the two-dimensional Ising model are sufficiently universal to predict the structure of …

Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19)
outbreak which is declared as a pandemic by the world health organization (WHO) is …

Variational quantum algorithms (VQAs) are widely applied in the noisy intermediate-scale
quantum era and are expected to demonstrate quantum advantage. However, training VQAs …