11 papers
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…
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…
Majorana string simulation of nonequilibrium dynamics in two-dimensional lattice fermion systems
Matteo D'Anna, Jannes Nys, Juan Carrasquilla
The study of real-time dynamics of fermions remains one of the last frontiers beyond the reach of classical simulations and is key to our understanding of quantum behavior in chemi…
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…