Overcoming the Representational Limitations of Amplitude Encoding for Quantum Neural Networks
Quantum Neural Networks (QNNs) offer a framework for learning functions from classical data, but their expressive power depends critically on how inputs are encoded into quantum states. Angle encoding can require resources that scale with the number of features, whereas amplitude encoding represents $d$ features using...
Vincent Gurgul, D. Prinz
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