| Abstract [eng] |
In recent years, governments, universities, and private companies have invested heavily in quantum computing. It is hoped that practical quantum technology can be developed that can perform classical computing on quantum computers. As Preskill writes in his articles, quantum research is entering a new frontier, where the simulation of quantum states in classical systems is expected to speed up the solution of practical problems significantly (super-polynomial or exponential). In this work, an attempt is made to analyze the behavior of some quantum circuits in a simulated quantum-classical system. The experiments generate multiclass data with a fixed number of classes and a variable number of features. Classical and quantum machine learning algorithms are implemented. Both approaches are compared. In addition, it describes how quantum circuits are constructed and how they can be modeled using some popular Python libraries. A study of algorithm timing and circuit complexity related to quantum circuit algorithms is also presented. |
