Quantum Computing And Data Mining Recent Advancements: A Review

A significant amount of interest has been generated in recent years in the convergence of quantum computing and data mining due to quantum algorithms' potential to revolutionize information extraction from vast datasets, as well as their ability to utilize quantum principles and natural capabilities to perform far more efficiently and efficiently than classic algorithms. We provide a comprehensive and detailed review of the current state of quantum computing algorithms in the field of data mining and machine learning, highlighting key developments, challenges, and their implementation. In particular, machine learning can benefit from QC mechanics like superposition and entanglement due to the use of q-bits as well as other superior advantages provided by quantum computing, such as Grover algorithm. It is important to note that classic machine learning models differ in terms of the types of learning, prediction models, and operating on data as well as performance over time, along with their counterparts, such as supervised and unsupervised models, which are naturally more sophisticated. Our review will also discuss how integrating and merging the two fields of machine learning and quantum computing, and their mechanics, will affect the aspect of time and resource