Variational quantum solver employing the PDS energy functional

TL;DR

This paper introduces a novel variational quantum solver based on the PDS energy functional, which enhances accuracy and avoids local minima in quantum simulations of molecular systems.

Contribution

It develops a new variational quantum algorithm utilizing the PDS energy gradient, improving upon VQE and static PDS in accuracy and efficiency.

Findings

Outperforms VQE and static PDS in toy and molecular models

Achieves high accuracy with modest trial wave functions

Demonstrates effectiveness on NISQ devices

Abstract

Recently a new class of quantum algorithms that are based on the quantum computation of the connected moment expansion has been reported to find the ground and excited state energies. In particular, the Peeters-Devreese-Soldatov (PDS) formulation is found variational and bearing the potential for further combining with the existing variational quantum infrastructure. Here we find that the PDS formulation can be considered as a new energy functional of which the PDS energy gradient can be employed in a conventional variational quantum solver. In comparison with the usual variational quantum eigensolver (VQE) and the original static PDS approach, this new variational quantum solver offers an effective approach to navigate the dynamics to be free from getting trapped in the local minima that refer to different states, and achieve high accuracy at finding the ground state and its energy through the rotation of the trial wave function of modest quality, thus improves the accuracy and efficiency of the quantum simulation. We demonstrate the performance of the proposed variational quantum solver for toy models, H$_2$ molecule, and strongly correlated planar H$_4$ system in some challenging situations. In all the case studies, the proposed variational quantum approach outperforms the usual VQE and static PDS calculations even at the lowest order. We also discuss the limitations of the proposed approach and its preliminary execution for model Hamiltonian on the NISQ device.