Variational Quantum Algorithms

TL;DR

Variational Quantum Algorithms are a promising approach to leverage near-term quantum devices for complex computations, addressing current hardware limitations through hybrid quantum-classical methods.

Contribution

This paper provides a comprehensive overview of VQAs, discusses strategies to overcome their challenges, and highlights their potential for achieving quantum advantage.

Findings

VQAs are applicable to a wide range of quantum computing problems.

Strategies to improve trainability and accuracy are discussed.

VQAs are considered the most promising near-term quantum algorithms.

Abstract

Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will likely not be available in the near future. Current quantum devices have serious constraints, including limited numbers of qubits and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which use a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisioned for quantum computers, and they appear to the best hope for obtaining quantum advantage. Nevertheless, challenges remain including the trainability, accuracy, and efficiency of VQAs. Here we overview the field of VQAs, discuss strategies to overcome their challenges, and highlight the exciting prospects for using them to obtain quantum advantage.