Efficient variational quantum eigensolver methodologies on quantum processors

TL;DR

This paper evaluates various variational quantum eigensolver (VQE) methods on IBM quantum hardware for the BeH2 molecule, demonstrating their effectiveness and potential for large molecule simulations despite noise.

Contribution

It compares adaptive, tetris-adaptive VQE, and entanglement forging methods, incorporating error mitigation techniques to enhance performance on noisy quantum processors.

Findings

VQE methods are effective on noisy hardware.

Error mitigation improves VQE accuracy.

VQE can be scaled for larger molecules.

Abstract

We compare the performance of different methodologies for finding the ground state of the molecule BeH2. We implement adaptive, tetris-adaptive variational quantum eigensolver (VQE), and entanglement forging to reduce computational resource requirements. We run VQE experiments on IBM quantum processing units and use error mitigation, including twirled readout error extinction (TREX) and zero-noise extrapolation (ZNE) to reduce noise. Our results affirm the usefulness of VQE on noisy quantum hardware and pave the way for the usage of VQE related methods for large molecules.