Quantum Computers Aids in Data Accessibility with Machine Language


Researchers at Purdue University, combined quantum algorithms with phasor measurement units, to speed up database accessibility.

Scientists analyzing the United States’ electrical grid are working to find a solution for combining quantum algorithms with classical computing on small-scale quantum computers to increase the efficiency of its database accessibility. Data analysis on that scale is a challenge when crucial information is stored in an inaccessible database. Their findings were published in the journal Nature Communications on October 12, 2018.

“We have already developed a hybrid quantum algorithm employing a quantum Boltzmann machine to obtain accurate electronic structure calculations,” said Sabre Kais, professor of chemical physics and principal investigator.

The researchers used a unique method to enhance accessibility of data, which will boost a number of practical applications, such as helping industries optimize their supply-chain and logistics management. Using an artificial neural network known as a quantum Boltzmann machine, the team believes that the developed technology will aid new chemical and material discovery.

Alex Pothen, professor of computer science and co-investigator on the project, explained: “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.”


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