Quantum Machine Learning of Bigdata Set Using Randomized Measurements

Utilizing discriminative deep belief networks (DDBNs) within the framework of semi-supervised learning, this technique leverages a combination of a limited set of labeled samples to enhance intrusion prevention efficacy while reducing recognition errors. By integrating both non-local and local regularization techniques, the method effectively tackles the inherent challenge of high error rates in cyber prevention models. Additionally, this approach is designed for implementation on Quantum Computing platforms, harnessing advanced machine learning algorithms to significantly improve cybersecurity measures.