A Purdue University’s research team has embarked on a task to combine quantum computing and analysis of big data sets. How big? 3 petabytes of data is collected every two seconds. The data is coming from the U.S. electrical grid. Monitoring the data – currents and voltages – enables the grid operators to make decisions which keep the grid stable. To analyze such a tremendous amount of data requires unique systems invoking unique algorithms.
Quantum computing is being studied as the analytical solution. The team has created a hybrid quantum algorithm using a quantum Boltzmann machine – an artificial neural network used in data analysis and machine learning. Their method is being refined though has already been proven for ‘small molecular systems’. Looking forward, the Purdue team’s algorithms have been thought to apply to several complex problems. One, to keep the electric grid stable. Two, to better model chemical interactions. Three, “to determine the lifetime of solar farms and other sustainable energy technologies.” Though in the early stages of quantum computing research to this end, the team’s studies bear watching.