From the 1 of 8 linked papers with an AI index.
8 papers
Majorana string simulation of nonequilibrium dynamics in two-dimensional lattice fermion systems
Matteo D'Anna, Jannes Nys, Juan Carrasquilla
The paper presents a Heisenberg-picture algorithm that uses a Majorana-string representation to simulate real-time dynamics of fermionic systems, especially in two dimensions, with…
Real-Time Dynamics in Two Dimensions with Tensor Network States via Time-Dependent Variational Monte Carlo
Yantao Wu, Jannes Nys
Reliably simulating two-dimensional many-body quantum dynamics with projected entangled pair states (PEPS) has long been a difficult challenge. In this work, we overcome this barri…
Projected Inverse Iteration: An Eigenvalue Approach to Ground-State Computation with Neural Quantum States
Hang Zhang, Victor Armegioiu, Juan Carrasquilla +4
Deep learning offers a powerful approach to quantum many-body problems via neural network wavefunctions, but their optimization remains a severe bottleneck. Existing optimization m…
Stabilizer-based quantum simulation of fermion dynamics with local qubit encodings
Anthony Gandon, Samuele Piccinelli, Max Rossmannek +4
Simulating the dynamical properties of large-scale many-fermion systems is a longstanding goal of quantum chemistry, material science and condensed matter. Local fermion-to-qubit e…
Fermionic neural Gibbs states
Jannes Nys, Juan Carrasquilla
We introduce fermionic neural Gibbs states (fNGS), a variational framework for modeling finite-temperature properties of strongly interacting fermions. fNGS starts from a reference…
Accurate ground states of lattice gauge theory in 2+1D and 3+1D
Thomas Spriggs, Eliska Greplova, Juan Carrasquilla +1
We present a neural network wavefunction framework for solving non-Abelian lattice gauge theories in a continuous group representation. Using a combination of equivariant n…