24 citations · 39 across the 6 of their papers we have counts for
6 papers
Adaptive-basis sample-based neural diagonalization for quantum many-body systems
Simone Cantori, Luca Brodoloni, Edoardo Recchi +3
Accurately estimating ground-state energies of quantum many-body systems is still a challenging computational task because of the exponential growth of the Hilbert space with the s…
Deep-learned error mitigation via partially knitted circuits for the variational quantum eigensolver
Simone Cantori, Andrea Mari, David Vitali +1
The variational quantum eigensolver (VQE) is generally regarded as a promising quantum algorithm for near-term noisy quantum computers. However, when implemented with the deep circ…
Synergy between noisy quantum computers and scalable classical deep learning
Simone Cantori, Andrea Mari, David Vitali +1
We investigate the potential of combining the computational power of noisy quantum computers and of classical scalable convolutional neural networks (CNNs). The goal is to accurate…
Challenges and opportunities in the supervised learning of quantum circuit outputs
Simone Cantori, Sebastiano Pilati
Recently, deep neural networks have proven capable of predicting some output properties of relevant random quantum circuits, indicating a strategy to emulate quantum computers alte…
Supervised learning of random quantum circuits via scalable neural networks
S. Cantori, D. Vitali, S. Pilati
Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervis…
Scalable neural networks for the efficient learning of disordered quantum systems
N. Saraceni, S. Cantori, S. Pilati
Supervised machine learning is emerging as a powerful computational tool to predict the properties of complex quantum systems at a limited computational cost. In this article, we q…