Quantum computers tackle big data with machine learning

Researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility.

A Purdue research team led by Sabre Kais, professor of chemical physics, is combining quantum algorithms with classical computing to speed up database accessibility. Credit: Purdue University

Every two seconds, sensors measuring the United States' electrical grid collect 3 petabytes of data – the equivalent of 3 million gigabytes. Data analysis on that scale is a challenge when crucial information is stored in an inaccessible database.


But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. They are using data from the U.S. Department of Energy National Labs' sensors, called phasor measurement units, that collect information on the electrical power grid about voltages, currents and power generation. Because these values can vary, keeping the power grid stable involves continuously monitoring the sensors.
Alex Pothen, professor of computer science and co-investigator on the project said, "Non-quantum algorithms that are used to analyze the data can predict the state of the grid, but as more and more phasor measurement units are deployed in the electrical network, we need faster algorithms. Quantum algorithms for data analysis have the potential to speed up the computations substantially in a theoretical sense, but great challenges remain in achieving quantum computers that can process such large amounts of data."
The research team's method has potential for a number of practical applications, such as helping industries optimize their supply-chain and logistics management. It could also lead to new chemical and material discovery using an artificial neural network known as a quantum Boltzmann machine. This kind of neural network is used for machine learning and data analysis.
"We have already developed a hybrid quantum algorithm employing a quantum Boltzmann machine to obtain accurate electronic structure calculations. We have proof of concept showing results for small molecular systems, which will allow us to screen molecules and accelerate the discovery of new materials," said Kais.
The research is published in the journal Nature Communications.

For further information go through original artcle on Phys.org.

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Scien-Tech News: Quantum computers tackle big data with machine learning
Quantum computers tackle big data with machine learning
Researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility.
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