Have you ever wondered about beating the time? And solving a large number of mathematical matrices, data and queries within just a fraction of second. Then without dilemma, there comes a word “

quantum”.

Quantum computers unseal parallelism leading to the reduction of steps required to solve the problems. It uses the qubits that can be represented by the electrons orbiting the nucleus. As compared to our classical computers which work on bits and lies either in zero states or one state but not both at the same time, something strange happens with quantum computers.

The electrons of the qubits fall…

Life is a Gradient Descent. We set up the goals and we gradually move to achieve them from our current position. Similarly, different events in our lives are influenced by gradient descent. Like you walking the smallest distance from home to school, choosing the shortest path for reaching down at the base of the camp, predicting the stock in the stock market for getting less loss etc. We want all these things to occur with precision and accuracy. …

Data of higher dimensions tend to create sparse matrixes and make it much more difficult to compute, analyze and visualize. It is therefore very important to reduce the dimensions to minimize redundant information, improve visualization and discover hidden correlated information. One of these methods of dimension reduction is PCA.

The principal component analysis also referred to as the K-L or Karhunen-Loeve method is the technique of reducing the dimensions of data without losing a lot of information from data. It searches for k n-dimensional orthogonal vectors that can be used to represent the data where k≤n.

The basic procedure for…