Evaluating the impact of noise on the performance of the Variational Quantum Eigensolver

TL;DR

This paper investigates how noise affects the Variational Quantum Eigensolver's ability to accurately compute molecular ground states on NISQ devices, analyzing optimizer performance and noise sources across different circuit architectures.

Contribution

It provides a systematic analysis of noise impact on VQE performance, compares optimizers, and models the relationship between noise levels and energy accuracy for various ansatzes.

Findings

NFT optimizer performs best under noise.

Noise significantly degrades energy estimation accuracy.

Circuit architecture influences noise sensitivity.

Abstract

Quantum computers are expected to be highly beneficial for chemistry simulations, promising significant improvements in accuracy and speed. The most prominent algorithm for chemistry simulations on NISQ devices is the Variational Quantum Eigensolver (VQE). It is a hybrid quantum-classical algorithm which calculates the ground state energy of a Hamiltonian based on parametrized quantum circuits, while a classical optimizer is used to find optimal parameter values. However, quantum hardware is affected by noise, and it needs to be understood to which extent it can degrade the performance of the VQE algorithm. In this paper, we study the impact of noise on the example of the hydrogen molecule. First, we compare the VQE performance for a set of various optimizers, from which we find NFT to be the most suitable one. Next, we quantify the effect of different noise sources by systematically increasing their strength. The noise intensity is varied around values common to superconducting devices of IBM Q, and curve fitting is used to model the relationship between the obtained energy values and the noise magnitude. Since the amount of noise in a circuit highly depends on its architecture, we perform our studies for different ansatzes, including both hardware-efficient and chemistry-inspired ones.