Monte Carlo Wavefunctions

Dario Bressanini, Pietro Cremaschi, Massimo Mella +1 more

Xinxin Du, Courtney Lannert

Klaus Mølmer, Yvan Castin

In this paper we present a review of the Monte Carlo wavefunction method. We discuss some aspects of its application in numerical simulations, and we comment on some of its relations to the foundations of quantum physics. Finally, we investigate the generalization to problems that have so far not been considered tractable by this method - in particular, nonlinear master equations may become relevant, and we discuss the application of Monte Carlo wavefunctions to such problems.

Billington, C. J., C. J. Watkins, R. P. Anderson +1 more

We present a Monte Carlo wavefunction method for semiclassically modeling spin-$\frac12$ systems in a magnetic field gradient in one dimension. Our model resolves the conflict of determining what classical force an atom should be subjected to when it is in an arbitrary superposition of internal states. Spatial degrees of freedom are considered to be an environment, entanglement with which decoheres the internal states. Atoms follow classical trajectories through space, punctuated by probabilistic jumps between spin states. We modify the conventional Monte Carlo wavefunction method to jump between states when population transfer occurs, rather than when population is later discarded via exponential decay. This results in a spinor wavefunction that is continuous in time, and allows us to model the classical particle trajectories (evolution of the environment variables) more accurately. The model is not computationally demanding and it agrees well with simulations of the full spatial wavefunction of an atom.

H. DeRaedt, Ad Lagendijk, Pedro de Vries

Yu-Sheng Li, Saskia Põldmaa, Tze Tzen Ong +4 more

In this work, we show that Fermi Sets---a provably universal neural network architecture for fermionic wavefunctions---can be systematically scaled up to find interacting ground states through energy minimization in a variational Monte Carlo framework. By further performing fixed-phase diffusion Monte Carlo (DMC) on the optimized neural network wavefunction, we demonstrate that as the network size increases, the variational energy systematically decreases while the energy improvement from DMC collapses monotonically to zero, indicating convergence to the ground state. We illustrate the scaling of Fermi Sets accompanied by the DMC assessment for interacting electrons in jellium and in a quantum dot under high magnetic fields.