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Equivariant quantum circuits for learning on weighted graphs - npj Quantum Information

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Equivariant quantum circuits for learning on weighted graphs - npj Quantum Information

Variational quantum algorithms are the leading candidate for advantage on near-term quantum hardware. When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm. In quantum machine learning (QML), however, the literature on ansatzes that are motivated by the training data structure is scarce.

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Equivariant quantum circuits for learning on weighted graphs - npj Quantum Information

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Equivariant quantum circuits for learning on weighted graphs - npj Quantum Information

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