2 citations · 2 across the 15 of their papers we have counts for
17 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,…
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
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…
Unbiased sampling from Boltzmann distributions with noisy energies
Iwo Sanderski, Gian Gentinetta, Giuseppe Carleo
Sampling from the Boltzmann distribution is central to computational physics, yet hard when the energy is known only through a stochastic estimate, such as with machine-learned mol…
A Fixed Universal Determinant is Variationally Complete for Continuum Fermions
Giuseppe Carleo, Riccardo Rossi
How many Slater determinants does an accurate variational description of interacting fermions require? Exact expansions in a finite basis need combinatorially many, and state-of-th…
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…
Variational low-energy subspaces for chemically accurate excited states
Clemens Giuliani, Rocco Martinazzo, Giuseppe Carleo +1
Accurate electronic excited states are essential for photochemistry, spectroscopy and non-adiabatic molecular dynamics, but high-level calculations often scale steeply and require…
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…
Quantum Finite Temperature Lanczos Method
Gian Gentinetta, Friederike Metz, William Kirby +1
The computation of thermal properties of quantum many-body systems is a central challenge in our understanding of quantum mechanics. We introduce the Quantum Finite Temperature Lan…
Variance reduction for forces and pressure in variational Monte Carlo
David Linteau, Saverio Moroni, Giuseppe Carleo +1
We present simple and practical strategies to reduce the variance of Monte Carlo estimators. Our focus is on variational Monte Carlo calculations of atomic forces and pressure in e…
Quantum simulation in the Heisenberg picture via vectorization
Shao-Hen Chiew, Armando Angrisani, Zoë Holmes +1
We present a general framework for simulating quantum systems in the Heisenberg picture on quantum hardware. Based on the vectorization map, our framework fully exploits the mappin…
Neural-network quantum states for the nuclear many-body problem
Alessandro Lovato, Giuseppe Carleo, Bryce Fore +4
A long-standing goal of nuclear theory is to explain how the structure and dynamics of atomic nuclei and neutron-star matter emerge from the underlying interactions among protons a…
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…
Grassmann Variational Monte Carlo with neural wave functions
Douglas Hendry, Alessandro Sinibaldi, Giuseppe Carleo
Excited states play a central role in determining the physical properties of quantum matter, yet their accurate computation in many-body systems remains a formidable challenge for…
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…
A circuit-differentiation framework for Green's functions on quantum computers
Samuele Piccinelli, Francesco Tacchino, Ivano Tavernelli +1
We propose a general framework for computing Retarded Green's Functions (RGFs) on quantum computers by recasting their evaluation as a problem of circuit differentiation. Our propo…