Models of quantum deep learning: SW algorithmic toolkit of quantum “strong” computational AI for IT beginners

The goal of machine learning (ML) is to train a computer to extract certain properties from a given data set without explicit coding or a set of rules, and then applying the results to learn these properties from new data for the purpose of prediction, classification, or developing a model of the object under study. The most popular models of machine learning are discussed, such as supervised learning, or learning with a teacher, in which the machine is pre-trained using some labeled data. Other forms of learning, such as unsupervised and reinforced, have also been widely studied and applied in various fields. Three of the most widely used supervised machine learning algorithms related to quantum computing are (a) neural networks (NN) for quantum logic synthesis, physical mapping and quantum error decoding, quantum key distribution (QKD) protocol, quantum ML accelerator, quantum neural networks (QNN); (b) reinforcement learning (RL) for quantum error decoding and (c) support vector machine (SVM) for quantum machine learning. The study also discusses various ML learning models including random search method for quantum communication. The work is intended to improve the skills of IT specialists applying “strong” AI methods.