Entanglement forging based variational algorithms leverage the bi-partit...
Quantum state reconstruction using Neural Quantum States has been propos...
Neural network approaches to approximate the ground state of quantum
ham...
Machine learning and specifically deep-learning methods have outperforme...
We introduce the Gram-Hadamard Density Operator (GHDO), a new deep
neura...
We show that any matrix product state (MPS) can be exactly represented b...
We introduce version 3 of NetKet, the machine learning toolbox for many-...
We initiate the study of neural-network quantum state algorithms for
ana...
Efficient sampling of complex high-dimensional probability densities is ...
We establish a direct connection between general tensor networks and dee...
Gauge symmetries play a key role in physics appearing in areas such as
q...
First-quantized deep neural network techniques are developed for analyzi...
A notion of quantum natural evolution strategies is introduced, which
pr...
A quantum generalization of Natural Gradient Descent is presented as par...
Artificial Neural Networks were recently shown to be an efficient
repres...
Studying general quantum many-body systems is one of the major challenge...