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Neural Wave Functions for High-Pressure Atomic Hydrogen
David Linteau, Saverio Moroni, Giuseppe Carleo +1
We leverage the power of neural quantum states to describe the ground state wave function of solid and liquid atomic hydrogen, including both electronic and protonic degrees of fre…
Unusual energy spectra of matrix product states
J. Maxwell Silvester, Giuseppe Carleo, Steven R. White
In approximate ground states obtained from imaginary-time evolution, the spectrum of the state -- its decomposition into exact energy eigenstates -- falls off exponentially with th…
Unveiling nonmagnetic phase and many-body entanglement in two-dimensional random quantum magnets SrCuTeWO
Dian Wu, Fan Yang, Giuseppe Carleo
We apply a random-plaquette - model on the square lattice to capture the physics of a series of spin- Heisenberg antiferromagnet compounds SrCuTeWO$_…
Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations
Imelda Romero, Jannes Nys, Giuseppe Carleo
Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice s…
Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation
Jannes Nys, Gabriel Pescia, Alessandro Sinibaldi +1
Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials,…
Variational solutions to fermion-to-qubit mappings in two spatial dimensions
Jannes Nys, Giuseppe Carleo
Through the introduction of auxiliary fermions, or an enlarged spin space, one can map local fermion Hamiltonians onto local spin Hamiltonians, at the expense of introducing a set…
Matrix Product States with Backflow correlations
Guglielmo Lami, Giuseppe Carleo, Mario Collura
By taking inspiration from the backflow transformation for correlated systems, we introduce a novel tensor network ansatz which extend the well-established Matrix Product State rep…
Fermionic Wave Functions from Neural-Network Constrained Hidden States
Javier Robledo Moreno, Giuseppe Carleo, Antoine Georges +1
We introduce a systematically improvable family of variational wave functions for the simulation of strongly correlated fermionic systems. This family consists of Slater determinan…
Role of stochastic noise and generalization error in the time propagation of neural-network quantum states
Damian Hofmann, Giammarco Fabiani, Johan H. Mentink +2
Neural-network quantum states (NQS) have been shown to be a suitable variational ansatz to simulate out-of-equilibrium dynamics in two-dimensional systems using time-dependent vari…
Hamiltonian reconstruction as metric for variational studies
Kevin Zhang, Samuel Lederer, Kenny Choo +3
Variational approaches are among the most powerful modern techniques to approximately solve quantum many-body problems. These encompass both variational states based on tensor or n…
Broken-Symmetry Ground States of the Heisenberg model on the Pyrochlore Lattice
Nikita Astrakhantsev, Tom Westerhout, Apoorv Tiwari +5
The spin-1/2 Heisenberg model on the pyrochlore lattice is an iconic frustrated three-dimensional spin system with a rich phase diagram. Besides hosting several ordered phases, the…
Gauge equivariant neural networks for quantum lattice gauge theories
Di Luo, Giuseppe Carleo, Bryan K. Clark +1
Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials. Mot…
Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states
James Stokes, Javier Robledo Moreno, Eftychios A. Pnevmatikakis +1
First-quantized deep neural network techniques are developed for analyzing strongly coupled fermionic systems on the lattice. Using a Slater-Jastrow inspired ansatz which exploits…
Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States
Kenny Choo, Titus Neupert, Giuseppe Carleo
The use of artificial neural networks to represent quantum wave-functions has recently attracted interest as a way to solve complex many-body problems. The potential of these varia…
Ground state phase diagram of the one-dimensional Bose-Hubbard model from restricted Boltzmann machines
Kristopher McBrian, Giuseppe Carleo, Ehsan Khatami
Motivated by recent advances in the representation of ground state wavefunctions of quantum many-body systems using restricted Boltzmann machines as variational ansatz, we utilize…
Symmetries and many-body excited states with neural-network quantum states
Kenny Choo, Giuseppe Carleo, Nicolas Regnault +1
Artificial neural networks have been recently introduced as a general ansatz to compactly represent many- body wave functions. In conjunction with Variational Monte Carlo, this ans…
The itinerant ferromagnetic phase of the Hubbard model
Giuseppe Carleo, Saverio Moroni, Federico Becca +1
Using a newly developed quantum Monte Carlo technique, we provide strong evidence for the stability of a saturated ferromagnetic phase in the high-density regime of the two-dimensi…