Computers assess information is by analyzing relationships within large sets of data. Researchers from the Centre for Quantum Technologies at the National University of Singapore have shown that quantum computers can perform one such analysis faster than classical computers, for a wider array of data types, than was previously expected. This is due to an new algorithm.
The new algorithm is called the “quantum linear system algorithm” and it could, one day, help computers to crunch numbers on complex problems ranging from commodities pricing to social networks and chemical structures.
According to the lead researcher, Dr. Zhikuan Zhao standard computing cannot adequately cope with such complex computations: “There is a lot of computation involved in analyzing the matrix. When it gets beyond say 10,000 by 10,000 entries, it becomes hard for classical computers”, hence the need for a new type of algorithm.
Taking a 10,000 square matrix, a classical algorithm would take on the order of a trillion computational steps; whereas the new quantum algorithm being worked on could process this level of data in only 100s of steps. Currently the algorithm is at the proof-of-principle stage ready for the new generation of quantum computers that are expected within the next five years.
Research into the algorithm has been published in the journal Physical Review Letters, with the peer-reviewed paper titled “Quantum Linear System Algorithm for Dense Matrices.”
In related algorithm news, U.S. Army researchers have developed new techniques for robots or computer programs to learn how to perform tasks by interacting with a human instructor. The class of machine learning algorithms used for this are inspired by the brain to provide a robot the ability to learn how to perform tasks by viewing video streams in a short amount of time with a human trainer.
