Classical and quantum cost of measurement strategies for quantum-enhanced auxiliary field Quantum Monte Carlo

TL;DR

This paper analyzes the classical and quantum costs of measurement strategies in quantum-enhanced auxiliary field quantum Monte Carlo, focusing on measurement overhead, variance behavior, and error propagation in overlap estimations.

Contribution

It provides a detailed comparison of classical shadow measurement schemes and their scaling, along with numerical insights into variance and error propagation in QC-AFQMC.

Findings

Classical post-processing scales as O(N^9) per walker.

Variance of classical shadows depends on ensemble choice and covariances.

Overlap estimation errors affect energy calculations and scale with system size.

Abstract

Quantum-enhanced auxiliary field quantum Monte Carlo (QC-AFQMC) uses output from a quantum computer to increase the accuracy of its classical counterpart. The algorithm requires the estimation of overlaps between walker states and a trial wavefunction prepared on the quantum computer. We study the applicability of this algorithm in terms of the number of measurements required from the quantum computer and the classical costs of post-processing those measurements. We compare the classical post-processing costs of state-of-the-art measurement schemes using classical shadows to determine the overlaps and argue that the overall post-processing cost stemming from overlap estimations scales like $\mathcal{O}(N^9)$ per walker throughout the algorithm. With further numerical simulations, we compare the variance behavior of the classical shadows when randomizing over different ensembles, e.g., Cliffords and (particle-number restricted) matchgates beyond their respective bounds, and uncover the existence of covariances between overlap estimations of the AFQMC walkers at different imaginary time steps. Moreover, we include analyses of how the error in the overlap estimation propagates into the AFQMC energy and discuss its scaling when increasing the system size.