Gate Function

Gate functions are mathematical functions controlling information flow within computational models, impacting efficiency and accuracy. Current research focuses on optimizing gate functions for various applications, including improving the training of recurrent neural networks (RNNs) by mitigating the saturation problem and enhancing quantum computing algorithms like quantum kernel estimation (QKE) for improved classification. These advancements aim to improve the efficiency and performance of both classical and quantum machine learning models, leading to more powerful and resource-efficient algorithms across diverse fields.