​Noticeable from the LHC experiments were lack of adequate processing power and storage infrastructures. Even with the China’s supercomputer Tianhe-2 (MilkyWay-2), which is the fastest computer in the world, the rapid increase in the complexity, size and dimensions of data is forcing the need to find a faster processing power that can go beyond treating data as binary digits (0’s or 1’s)! Statistically, in analyzing independent variables in modeling, we in analytics “throw” away independent variables as “insignificant”. The entanglement nature in quantum computing can potentially be used to process the correlated part of the “insignificant” independent variables so as to increase the coefficient of determination, the R-squared or percent of variance explained of the dependent variable.

The superposition principle becomes the precursor for addition of two time series. How awesome that will be for robust predictive analytics of business and stock market performances! With entanglement and superposition, a quantum computing can process a vast number of calculations simultaneously. Classical computer works with ones and zeros, a quantum computer has the advantage of using q-bits or ones, zeros and “superpositions” of ones and zeros. Certain difficult tasks that have long been thought impossible or intractable for classical computers can now be achieved quickly and efficiently with quantum computing.

Quantum computing will be faster in an environment where there is the need for Markov Chain Monte Carlo (MCMC) based Bayesian statistics with large datasets to generate large hierarchical models that require integrations of hundreds or even thousands of unknown parameters. So also will quantum computing be friendlier with complex machine learning algorithms. The Grover’s search algorithm, Shor’s factoring algorithm and the rapid advances in D-Wave’s processors are affirming the possibilities of high level reasoning in decision making processes that are in the pipeline for the next big future in data management, analytics, modeling, machine learning and business intelligence.

Can exascale computers take us to where we can handle more complex calculations better than where we are today? With the US President’s order of July 29th, 2015 to build the exascale computers which will overtake China’s supercomputer Tianhe-2 (MilkyWay-2) to become the fastest computer in the world, there is the hope that the exascale computers may end up imitating the level of performance expected from quantum computers or even better!