A team of researchers at Los Alamos National Laboratory and Google has reviewed the state of the art of Variational Quantum Algorithms (VQAs).
Applications such as simulating large quantum systems or solving large-scale linear algebra problems are immensely challenging for classical computers due their extremely high computational cost. Currently available quantum devices have serious constraints, including limited qubit numbers and noise processes that limit circuit depth. Variational Quantum Algorithms which employ 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.
In this paper, they discuss the framework of Variational Quantum Algorithms, their applications, their challenges and potential solutions, and their prospects for obtaining quantum advantage.
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