Recent Developments in VQE: Survey and Benchmarking

TL;DR

This paper surveys recent developments in the Variational Quantum Eigensolver (VQE), highlighting different algorithmic adaptations, benchmarking accuracy, and the current state of quantum simulators for near-term quantum computing.

Contribution

It provides a comprehensive overview of VQE variants, benchmarking efforts, and the status of quantum simulators, offering insights into progress and challenges in the field.

Findings

VQE variants reduce circuit complexity and improve scalability.

Benchmarking shows varying accuracy levels across methods.

Quantum simulators are advancing but still face limitations.

Abstract

The Variational Quantum Eigensolver (VQE) algorithm has been developed to target near term Noisy Intermediate Scale Quantum (NISQ) computers as a method to find the eigenvalues of Hamiltonians. Unlike fully quantum algorithms such as Quantum Phase Estimation (QPE), VQE based methods are hybrid algorithms that utilize both quantum and classical hardware to combat issues with the near term quantum hardware such as small numbers of available qubits and the decoherence of qubits. Different adaptations (flavors) of VQE have been implemented to combat these scalability issues on NISQ devices compared to standard VQE. These different flavors are modifications of the underlying VQE ansatz to reduce the computational workload on the quantum hardware. In this review we focus on 3 main areas related to VQE. The first focus is on flavors of VQE that fall under the categories of circuit complexity reduction, chemistry inspired ansatz, and extensions of VQE to excited states. The remaining portion of the review focuses on benchmarking the accuracy of VQE methods and an overview of the current state of quantum simulators.