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Variational quantum algorithms
Alexander M. Dalzell, Sam McArdle, Mario Berta (RWTH Aachen University), Przemyslaw Bienias, Chi-Fang Chen (University of California, Berkeley), András Gilyén (HUN-REN Alfréd Rényi Institute of Mathematics, Hungarian Research Network), Connor T. Hann, Michael J. Kastoryano (University of Copenhagen), Emil T. Khabiboulline (National Institute of Standards and Technology), Aleksander Kubica (Yale University), Grant Salton (Amazon (United States)), Samson Wang (California Institute of Technology), Fernando G. S. L. Brandão
Abstract: This chapter covers variational quantum algorithms, which act as a primitive ingredient for larger quantum algorithms in several application areas, including quantum chemistry, combinatorial optimization, and machine learning. Variational quantum algorithms are parameterized quantum circuits where the parameters are trained to optimize a certain cost function. They are often shallow circuits, which potentially makes them suitable for near-term devices that are not error corrected.