Variational Monte Carlo

Variational Monte Carlo (VMC) is a computational method used to approximate solutions to the Schrödinger equation, a fundamental problem in quantum mechanics, by minimizing the energy of a parameterized wavefunction. Current research focuses on improving the efficiency and accuracy of VMC, particularly through the use of neural networks (like FermiNet, Psiformer, and transformer-based architectures) as flexible wavefunction ansätze and advanced optimization techniques such as natural gradient descent and Kaczmarz-inspired methods. These advancements enable more accurate calculations of ground and excited states for increasingly complex systems, impacting fields like quantum chemistry and materials science by providing highly accurate predictions of molecular properties and material behaviors.