Optimizing large parameter sets in variational quantum Monte Carlo

TL;DR

This paper introduces an efficient method for optimizing extremely large sets of variational parameters in quantum Monte Carlo simulations, enabling high-accuracy results for complex systems.

Contribution

It presents a novel iterative Krylov subspace solver approach that bypasses matrix construction, allowing optimization of hundreds of thousands of parameters in variational quantum Monte Carlo.

Findings

Achieved 1% energy accuracy in 2D Hubbard model systems.

Recovered over 98% of correlation energy in hydrogen lattice.

Predicted phase separation and computed energy gaps with high precision.

Abstract

We present a technique for optimizing hundreds of thousands of variational parameters in variational quantum Monte Carlo. By introducing iterative Krylov subspace solvers and by multiplying by the Hamiltonian and overlap matrices as they are sampled, we remove the need to construct and store these matrices and thus bypass the most expensive steps of the stochastic reconfiguration and linear method optimization techniques. We demonstrate the effectiveness of this approach by using stochastic reconfiguration to optimize a correlator product state wavefunction with a pfaffian reference for four example systems. In two examples on the two dimensional Hubbard model, we study 16 and 64 site lattices, recovering energies accurate to 1% in the smaller lattice and predicting particle-hole phase separation in the larger. In two examples involving an ab initio Hamiltonian, we investigate the potential energy curve of a symmetrically dissociated 4x4 hydrogen lattice as well as the singlet-triplet gap in free base porphin. In the hydrogen system we recover 98% or more of the correlation energy at all geometries, while for porphin we compute the gap in a 24 orbital active space to within 0.02eV of the exact result. The numbers of variational parameters in these examples range from 4x10^3 to 5x10^5, demonstrating an ability to go far beyond the reach of previous formulations of stochastic reconfiguration.