Speaker
Suryansh Rajawat
(University of Maryland)
Description
We present a variational approach to quantum field theory based on wavefunctions parameterized by neural networks, as a stepping stone towards real-time and finite-density regimes, inaccessible to path-integral Monte Carlo. Working in the Hamiltonian formulation on a spatial lattice, we optimize a neural-network ansatz with variational Monte Carlo to obtain the ground-state and excited-state wavefunctions. As a proof of principle, we study the 1+1d nonlinear sigma model and reproduce its essential features: asymptotic freedom, dynamical mass generation, and the model's step-scaling curve. Although energy minimization is dominated by short-distance modes, the trained wavefunction nonetheless captures long-distance physics.
Authors
Gregory Ridgway
(University of Maryland)
Hersh Kumar
(University of Maryland)
Paulo Bedaque
(University of Maryland)
Suryansh Rajawat
(University of Maryland)