For a long time, classical computers have been used to realize complex systems, but with time, the complexity of the circuits increased. We started to witness the limitations of this type of computing, resulting in a lack of appropriate analysis and a vast runtime for the running code. Most of all, there are inaccurate results for a massive data pool. Classical computers, which rely on bits to represent data as 0 or 1, have been the foundation for handling complex applications like artificial intelligence, GPS tracking, and large-scale simulations. However, as the complexity of problems increases, especially in areas such as AI, climate modeling, and drug discovery, classical systems face limitations in processing power and efficiency with the increase in data pool and for faster processing time with more accuracy, a new method sought after a method which can provide parallel computation of data and can handle large pool of data at ease handling the unique exceptions keeping these concerns in mind many pioneers started to look after alternative to classical computation methods. In contrast, many believed in the power of classical computation and tried to improve its efficiency and usage, while others started looking into alternatives.