Variational Quantum Algorithms
This paper is a review of existing methods for quantum computing researchers, providing an overview of VQAs and their challenges.
This paper overviews Variational Quantum Algorithms (VQAs), which address the limitations of current quantum computers by using classical optimizers to train parametrized quantum circuits. VQAs are proposed for a wide range of quantum computing applications, aiming to achieve quantum advantage despite challenges in trainability, accuracy, and efficiency.
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.