An Analysis of First- and Quasi-Second-Order Optimization Algorithms in Variational Monte Carlo

Abstract

Many quantum many-body wavefunctions, such as Jastrow-Slater, tensor network, and neural quantum states, are studied with the variational Monte Carlo technique, where stochastic optimization is usually performed to obtain a faithful approximation to the ground-state of a given Hamiltonian. While first-order gradient descent methods are commonly used for such optimizations, quasi-second-order optimization formulations offer the potential of faster convergence under certain theoretical conditions, but with a similar cost per sample to first-order methods. However, the relative performance of first-order and second-order optimizers is influenced in practice by many factors, including the sampling requirements for a faithful optimization step, the influence of wavefunction quality, as well as the wavefunction parametrization and expressivity. Here we analyze these performance characteristics of first-order and quasi-second-order optimization methods for a variety of Hamiltonians, with the additional context of understanding the scaling of these methods (for good performance) as a function of system size. Our findings help clarify the role of first-order and quasi-second-order methods in variational Monte Carlo calculations and the conditions under which they should respectively be used. In particular, we find that unlike in deterministic optimization, where closeness to the variational minimum determines the suitability of second-order methods, in stochastic optimization the main factor is the overall expressivity of the wavefunction: quasi-second-order methods lead to an overall reduction in cost relative to first-order methods when the wavefunction is sufficiently expressive to represent the ground-state, even when starting far away from the ground state. This makes quasi-second-order methods an important technique when used with wavefunctions with arbitrarily improvable accuracy.