Overfitting in quantum neural networks (QNNs) occurs when the model learns the training data too well, capturing noise and outliers rather than the underlying pattern. This leads to poor performance on new, unseen data as the model becomes overly complex and specific to the training dataset. Balancing model complexity and generalization is crucial to ensure effective learning and performance in QNNs.
congrats on reading the definition of Overfitting in QNNs. now let's actually learn it.