An evolutionary algorithm optimizes initialization hyperparameters for quantum circuits, leading to faster convergence without worsening barren plateaus.
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A double-Bayesian framework derives an optimal learning rate for neural network training via two antagonistic Bayesian processes.
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On the importance of hyperparameters in initializing parameterized quantum circuits
An evolutionary algorithm optimizes initialization hyperparameters for quantum circuits, leading to faster convergence without worsening barren plateaus.
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Training Neural Networks with Optimal Double-Bayesian Learning
A double-Bayesian framework derives an optimal learning rate for neural network training via two antagonistic Bayesian processes.