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Quantum Algorithm Could Help AI Think Faster

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CQT's Jansen Zhao

CQT's Jansen Zhao and collaborators have shown that quantum computers can analyze data relationships faster than classical computers for a wider array of data types than previously expected.

Credit: Center for Quantum Technologies

Researchers at the Center for Quantum Technologies in Singapore propose a "quantum linear system algorithm" to help crunch numbers on a wide array of problems. They say a linear system algorithm works on a large matrix of data, calculating how strongly each feature is correlated with another by "inverting" the matrix so the information can be used to extrapolate into the future.

The team notes their first quantum algorithm could manage larger matrices, but only if the data in them is "sparse," or has limited relationships among the elements, which rarely occurs in real-world data. The researchers say the new algorithm is advertised as faster than both the classical and the previous quantum versions, without limits on the kind of data it works for.

For a 10,000-square matrix, the classical algorithm would assume the order of 1 trillion computational steps, the first quantum algorithm some ten thousands of steps, and the new quantum algorithm only hundreds of steps.

From Center for Quantum Technologies
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Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA


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