New Quantum ML algorithm could revolutionise Quantum AI before it even begins

One of the ways that intelligent computers and Artificial Intelligence (AI) platforms "think" is by analysing the relationships between and within large sets of data. Now, using a new type of Quantum Machine Learning (QML) algorithm, an international team have demonstrated that quantum computers can analyse a far wider array of data types than was previously expected. The details of the team's new "Quantum Linear System Algorithm," or QLSA, was published in Arvix, and in the future it could help crunch numbers on problems as varied as commodities pricing, social networks and chemical structures, and usher in a new era of Quantum AI. "Previous quantum algorithms only worked on very specific types of problem. We needed an upgrade if we want to achieve a quantum speed up for other data," said Zhikuan Zhao, who co-authored the paper, and that's exactly what he, and his colleagues, Anupam Prakash at the Centre for Quantum Technologies in Singapore, and Leonard Wossnig from ETH Zurich and the University of Oxford, have done. QLSA's were first proposed in 2009 by a different group of researchers and since then the idea's helped kick start research into new exotic forms of AI such as Quantum Artificial Intelligence (QAI), which gradually I'm seeing more and more research papers reference.