The Utilization of Quantum Computing Methods for the Implementation of Supervised Machine Learning Frameworks - Mukta Nivelkar; S. G. Bhirud (Theory and Applications of Engineering Research Vol. 2, 2024)
In this chapter, the integration of quantum computing and machine learning is given a lot of attention, which will make perfect sense when applied to the context of modelling quantum machine learning. The mechanism of quantum computing lends credence to the concept of numerous machine learning-related activities as possible applications in quantum technology. The goal of quantum computing is to develop a new standard of processing that is fundamentally different from that of traditional computers. This is accomplished by incorporating ideas from quantum physics, such as superposition and entanglement, into the computing process. In the first part of this chapter, we took a high-level look at some of the principles of quantum theory. In addition to that, an investigation into quantum machine learning has also been looked at in this article. The qubit is the most fundamental component of quantum technology and plays an important role in the implementation of quantum processes in a wide range of different fields of endeavour. The use of standard computing devices is rendered obsolete by the advent of quantum computing, which permits the resolution of issues that were previously intractable. Complicated computations refer to issues that are famously difficult to solve using typical computing methods. These problems are notoriously difficult to solve. Learning software that is based on traditional models performs incredibly well, but it comes with increasing requirements for computer power since it must handle a complex and extensive quantity of data. When modelling supervised machine learning with quantum computing, some of the work that must be done includes the selection of features, the encoding of parameters, and the building of parameterized circuits. Topics of conversation also include the modelling of quantum parameterized circuits, as well as the design and implementation of quantum feature sets for sample data. The application of quantum processes like as superposition and entanglement is used to illustrate the idea of guided machine learning.