<ul><li>Quantum machine learning has limitations due to linear unitary operations and shared trainable parameters across outputs.</li><li>Superposed parameterised quantum circuits are introduced to overcome these limitations by embedding an exponential number of parameterised sub-models in a single circuit.</li><li>This new architecture induces polynomial activation functions through amplitude transformations and post-selection, allowing for training multiple parameter sets in parallel.</li><li>Numerical experiments show significant advantages of superposed parameterised quantum circuits in reducing errors and improving accuracy in tasks like regression and classification.</li></ul>

Superposed Parameterised Quantum Circuits

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