91 citations · 254 across the 20 of their papers we have counts for
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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…
BayesPrompt: human readable prompts that make sense
Franky Kevin Nando Tezoh, Ali Hussaini Umar, Alessandro Laio +2
Reconstructing prompts that can elicit a desired answer or behaviour in an LLM is an open and important research topic. Optimisation methods which aim at minimising the perplexity…
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…