Implementation Of Grover's and Shor's Algorithms In Quantum Machine Learning

Abstract

Algorithms for quantum machine learning are responsible for identifying essential information and predicting outcomes of the raw data samples. Unlike other mathematical methods that rely on a predetermined model to make predictions, these algorithms use existing data to continuously improve their ability to predict outcomes. Quantum computing algorithms have the capability to solve complex problems by leveraging the power of parallel computing, which allows them to perform many calculations simultaneously. The effectiveness of quantum computing algorithms in machine learning allows quantum technology to reach an advanced level of improvement. This study examines application-based algorithms, namely Grover's algorithm and Shor's algorithm, which are essential and most widely used quantum machine learning algorithms.