91 citations · 222 across the 17 of their papers we have counts for
18 papers
Designing Strongly Correlated Quantum Phases of Matter with Foundation Neural-Network Quantum States
Alessandro Sinibaldi, Luciano Loris Viteritti, Riccardo Rende +2
Materials design is an inverse problem: finding the couplings of a microscopic Hamiltonian whose ground state exhibits a desired property. For strongly correlated quantum systems,…
Kibble-Zurek Dynamics in Two-dimensional Frustrated Systems with a Neural Foundation-state Subspace Method
Linda Mauron, Luciano Loris Viteritti, Zakari Denis +2
Universal scaling generated when a strongly interacting quantum many-body system is driven across a continuous phase transition provides a dynamical probe of equilibrium criticalit…
Superconductivity in the - Hubbard Model from Symmetry-Preserving Neural-Network Quantum States
Riccardo Rende, Luciano Loris Viteritti, Antoine Georges
Despite its fundamental importance in the theory of strongly correlated electrons, the nature of the ground state of the two-dimensional doped Hubbard model remains intensely debat…
Thermalization Dynamics in the Two-Dimensional Hubbard Model with Neural-Network Quantum States
Alessandro Sinibaldi, Luciano Loris Viteritti, Riccardo Rende +2
Thermalization in strongly correlated fermionic systems remains a central open problem in quantum many-body physics. In this work, we investigate the real-time dynamics and the app…
Fermi surface change and -wave superconductivity in the square lattice Kondo-Heisenberg model
Alexander Nikolaenko, Riccardo Rende, Luciano Loris Viteritti +2
We study the two-dimensional Kondo-Heisenberg model on a square lattice, with the conduction electrons away from half-filling, using neural network quantum states. Mapping the grou…
Scaling Laws for Neural-Network Quantum States
Riccardo Rende, Alessandro Sinibaldi, Luciano Loris Viteritti +3
Scaling laws, the power-law relations between loss, architecture size, and compute observed in modern neural networks, offer a quantitative way to characterize the complexity of a…
Beyond Variational Bias: Resolving Intertwined Orders in the Hubbard Model
Luciano Loris Viteritti, Riccardo Rende, Christopher Roth +3
The two-dimensional Hubbard model at finite doping hosts competing or intertwined orders, resulting in conflicting conclusions from different computational approaches regarding its…
Transformer Neural-Network Quantum States for lattice models of spins and fermions: Application to the Ancilla Layer Model
Riccardo Rende, Alexander Nikolaenko, Luciano Loris Viteritti +2
We introduce a variational wave function based on Neural-Network Quantum States (NQS) to study lattice systems whose local Hilbert space contains both spin and fermionic degrees of…
Approaching the Thermodynamic Limit with Neural-Network Quantum States
Luciano Loris Viteritti, Riccardo Rende, Subir Sachdev +1
Accessing the thermodynamic-limit properties of strongly correlated quantum matter requires simulations on very large lattices, a regime that remains challenging for numerical meth…
Quantum Spin Glass in the Two-Dimensional Disordered Heisenberg Model via Foundation Neural-Network Quantum States
Luciano Loris Viteritti, Riccardo Rende, Giacomo Bracci-Testasecca +4
We investigate the two-dimensional frustrated quantum Heisenberg model with bond disorder on nearest-neighbor couplings using the recently introduced Foundation Neural-Network Quan…
Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians
Riccardo Rende, Luciano Loris Viteritti, Federico Becca +3
Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like…
Are queries and keys always relevant? A case study on Transformer wave functions
Riccardo Rende, Luciano Loris Viteritti
The dot product attention mechanism, originally designed for natural language processing tasks, is a cornerstone of modern Transformers. It adeptly captures semantic relationships…
Fine-tuning Neural Network Quantum States
Riccardo Rende, Sebastian Goldt, Federico Becca +1
Recent progress in the design and optimization of neural-network quantum states (NQSs) has made them an effective method to investigate ground-state properties of quantum many-body…
Equivariant Variational Quantum Eigensolver to detect Phase Transitions through Energy Level Crossings
Giulio Crognaletti, Giovanni Di Bartolomeo, Michele Vischi +1
Level spectroscopy stands as a powerful method for identifying the transition point that delineates distinct quantum phases. Since each quantum phase exhibits a characteristic sequ…
Transformer Wave Function for two dimensional frustrated magnets: emergence of a Spin-Liquid Phase in the Shastry-Sutherland Model
Luciano Loris Viteritti, Riccardo Rende, Alberto Parola +2
Understanding quantum magnetism in two-dimensional systems represents a lively branch in modern condensed-matter physics. In the presence of competing super-exchange couplings, mag…
A simple linear algebra identity to optimize Large-Scale Neural Network Quantum States
Riccardo Rende, Luciano Loris Viteritti, Lorenzo Bardone +2
Neural-network architectures have been increasingly used to represent quantum many-body wave functions. These networks require a large number of variational parameters and are chal…
Transformer variational wave functions for frustrated quantum spin systems
Luciano Loris Viteritti, Riccardo Rende, Federico Becca
The Transformer architecture has become the state-of-art model for natural language processing tasks and, more recently, also for computer vision tasks, thus defining the Vision Tr…
Accuracy of Restricted Boltzmann Machines for the one-dimensional Heisenberg model
Luciano Loris Viteritti, Francesco Ferrari, Federico Becca
Neural networks have been recently proposed as variational wave functions for quantum many-body systems [G. Carleo and M. Troyer, Science 355, 602 (2017)]. In this work, we focus o…