A variational quantum circuit trained with noisy, clipped gradients is proposed as a privacy-preserving approximator for probabilistic optimal power flow.
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A Differentially Private Quantum Neural Network for Probabilistic Optimal Power Flow
A variational quantum circuit trained with noisy, clipped gradients is proposed as a privacy-preserving approximator for probabilistic optimal power flow.