Web: http://arxiv.org/abs/2205.06109

May 13, 2022, 1:11 a.m. | Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, Vedran Dunjko

cs.LG updates on arXiv.org arxiv.org

Variational quantum algorithms are the leading candidate for near-term
advantage on noisy quantum hardware. When training a parametrized quantum
circuit to solve a specific task, the choice of ansatz is one of the most
important factors that determines the trainability and performance of the
algorithm. Problem-tailored ansatzes have become the standard for tasks in
optimization or quantum chemistry, and yield more efficient algorithms with
better performance than unstructured approaches. In quantum machine learning
(QML), however, the literature on ansatzes that …

arxiv graphs learning on quantum

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