43 citations · 59 across the 4 of their papers we have counts for
4 papers
Solving deep-learning density functional theory via variational autoencoders
Emanuele Costa, Giuseppe Scriva, Sebastiano Pilati
In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilit…
Challenges of variational quantum optimization with measurement shot noise
Giuseppe Scriva, Nikita Astrakhantsev, Sebastiano Pilati +1
Quantum enhanced optimization of classical cost functions is a central theme of quantum computing due to its high potential value in science and technology. The variational quantum…
Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
Giuseppe Scriva, Emanuele Costa, Benjamin McNaughton +1
Adiabatic quantum computers, such as the quantum annealers commercialized by D-Wave Systems Inc., are routinely used to tackle combinatorial optimization problems. In this article,…
Deep learning density functionals for gradient descent optimization
Emanuele Costa, Giuseppe Scriva, Rosario Fazio +1
Machine-learned regression models represent a promising tool to implement accurate and computationally affordable energy-density functionals to solve quantum many-body problems via…