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 molecular potentials, in variational Monte Carlo, or on quantum computers, because a noisy energy biases the sampled distribution. The penalty...
Iwo Sanderski, Gian Gentinetta, Giuseppe Carleo· 0 citations
Designing a material with a desired property amounts to solving an inverse problem: finding the couplings of a microscopic Hamiltonian whose ground state exhibits that property. For strongly correlated quantum systems, solving this problem efficiently remains largely out of reach. To address this challenge, we present...
A. Sinibaldi, Luciano Loris Viteritti, Riccardo Rende et al.· 0 citations
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 widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging...
Jörg Behler, Michele Ceriotti, Cecilia Clementi et al.· 0 citations
Universal scaling generated when a strongly interacting quantum many-body system is driven across a continuous phase transition provides a dynamical probe of equilibrium criticality. Accessing this regime numerically in two dimensions is challenging because it requires accurate real-time evolution of correlated many-bo...
Linda Mauron, Luciano Loris Viteritti, Z. Denis et al.· 0 citations
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-the-art fermionic neural quantum states stack growing numbers of them. We prove that, in the norms that govern variational calculations, at mo...
Giuseppe Carleo, R. Rossi· 0 citations
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