Quantum information and artificial learning systems, for example, are cutting-edge technologies that could have a significant impact on our civilization in the future. The difficulties and challenges associated with quantum information, for example, differ significantly from those associated with artificial intelligence, machine learning, and other related fields. These issues have mostly been tackled separately until now. Many researchers are beginning to wonder whether or not these professions can learn from one another. Quantum computing theory is exploding right now, as is the classical machine learning theory of learning from experience. Researchers have recently looked into the possibility that quantum computing could aid in the improvement of current machine learning methods. Hybrid quantum machine learning makes use of quantum physics as well as classical and quantum algorithms. Quantum procedures, rather than classical data, can be used to analyze quantum states. Quantum algorithms, on the other hand, have the potential to improve classical data science techniques by an order of magnitude. We'll go over the fundamental concepts of quantum machine learning right now. The methods we offer combine classical machine learning algorithms with quantum computing techniques. Using IBM's quantum processor, this paper demonstrates how to implement a multiclass tree tensor network. We also present a quantum tomography problem method based on neural networks. It is possible for us to forecast the quantum state without taking noise into account thanks to our tomography technology. In many investigations, a classical-quantum technique can uncover latent dependence between input data and output measurement results.