Quantum Machine Learning and Quantum Communication Networks: The 2030s and the Future

Abstract

The two emerging technologies are machine learning and artificial intelligence, and as we all know, quantum computing is one of the most revolutionary developments in technology. Researchers are considering combining machine learning and quantum computing with the advancements in both fields. As a result, quantum machine learning-a fusion of these two fields-has evolved. It has the capacity to effectively address a wide range of real-world issues. Both fields will surely benefit from the combined results of the two fields. When the potential of quantum principles and its peculiarities is employed with machine learning, quantum machine learning reaches a very advanced level. Now that quantum concepts are being incorporated, traditional machine learning algorithms like SVM, PCA, and KNN are being reviewed as QSVM, Q-KNN, and QPCA, which are the most effective and powerful techniques for quantum machine learning. Quantum machine learning is a boom for the future; it will soon vastly surpass even the most advanced neural networks, deep learning systems, and machine learning systems of today. Modern machine learning is quicker than classical computing thanks to quantum machine learning, which is concerned with quantum software. Quantum data from an artificial quantum system is what QML is based on. In this paper, we will also study about the future network that is quantum network, which will be use by replacing classical networking. By enabling quantum communication between any two sites on Earth, a quantum internet is intended to fundamentally advance internet technology.