Benchmarking the Variational Quantum Eigensolver using different quantum hardware

TL;DR

This paper empirically compares the performance of the Variational Quantum Eigensolver on superconducting and ion trap quantum computers for hydrogen molecule simulation, analyzing hardware suitability and noise effects.

Contribution

It provides a systematic benchmarking of VQE on different quantum hardware architectures using a standardized setup, highlighting hardware-specific advantages and challenges.

Findings

Superconducting and ion trap quantum computers show different noise sensitivities.

Circuit depth and gate counts vary significantly between hardware types.

Empirical results inform hardware choice for VQE applications.

Abstract

The Variational Quantum Eigensolver (VQE) is a promising quantum algorithm for applications in chemistry within the Noisy Intermediate-Scale Quantum (NISQ) era. The ability for a quantum computer to simulate electronic structures with high accuracy would have a profound impact on material and biochemical science with potential applications e.g., to the development of new drugs. However, considering the variety of quantum hardware architectures, it is still uncertain which hardware concept is most suited to execute the VQE for e.g., the simulation of molecules. Aspects to consider here are the required connectivity of the quantum circuit used, the size and the depth and thus the susceptibility to noise effects. Besides theoretical considerations, empirical studies using available quantum hardware may help to clarify the question of which hardware technology might be better suited for a certain given application and algorithm. Going one step into this direction, within this work, we present results using the VQE for the simulation of the hydrogen molecule, comparing superconducting and ion trap quantum computers. The experiments are carried out with a standardized setup of ansatz and optimizer, selected to reduce the amount of iterations required. The findings are analyzed considering different quantum processor types, calibration data as well as the depth and gate counts of the circuits required for the different hardware concepts after transpilation.