A hybrid quantum-classical CNN for causality classification works only with a Pauli XYZ feature map, and deeper quantum ansatzes appear to act as implicit regularizers in single-run experiments.
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abstract
Grover's algorithm is usually described in terms of the iteration of a compound operator of the form $Q = - H I_{0} H I_{x_0}$. Although it is quite straightforward to verify the algebra of the iteration, this gives little insight into why the algorithm works. What is the significance of the compound structure of $Q$? Why is there a minus sign? Later it was discovered that $H$ could be replaced by essentially any unitary $U$. What is the freedom involved here? We give a description of Grover's algorithm which provides some clarification of these questions.
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quant-ph 1years
2025 1verdicts
REJECT 1representative citing papers
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Leveraging Quantum Layers in Classical Neural Networks
A hybrid quantum-classical CNN for causality classification works only with a Pauli XYZ feature map, and deeper quantum ansatzes appear to act as implicit regularizers in single-run experiments.