IBM finally proves that quantum systems are faster than classicals

Engadget

In 1994, MIT professor of applied mathematics, Peter Shor, developed a groundbreaking quantum computing algorithm capable of factoring numbers (that is, finding the prime numbers for any integer N) using quantum computer technology. For the next decade, this algorithm provided a tantalizing glimpse at the potential prowess of quantum computing versus classical systems. However researchers could never definitively prove that quantum would always be faster in this application or whether classical systems could overtake quantum if given a sufficiently robust algorithm of its own. In a paper published Thursday in the journal Science, Dr. Sergey Bravyi and his team reveal that they've developed a mathematical proof which, in specific cases, illustrates the quantum algorithm's inherent computational advantages over classical. "It's good to know, because results like this become parts of algorithms," Bob Sutor, vice president of IBM Q Strategy and Ecosystem, told Engadget.


The world's first quantum software superstore--or so it hopes--is here

MIT Technology Review

In quantum computing, it's not just the computers themselves that are hard to build. They also need sophisticated quantum algorithms--specialized software that's tailored to get the best out of the machines. Alán Aspuru-Guzik has gained an impressive reputation in academic circles by developing these kinds of algorithms, and now he's taking them to a wider market. A Harvard University professor (who's moving to the University of Toronto) and a 2010 member of MIT Technology Review's Innovators under 35 list, he is the cofounder of a company called Zapata Computing, which launched today with $5.4 million in announced funding. Zapata's ultimate goal is to be a kind of quantum-algorithm superstore, offering a broad range of ready-made software that companies can use to tap the immense processing power quantum computers promise to deliver.


Quantum Hype and Quantum Skepticism

Communications of the ACM

The first third of the 20th century saw the collapse of many absolutes. Albert Einstein's 1905 special relativity theory eliminated the notion of absolute time, while Kurt Gödel's 1931 incompleteness theorem questioned the notion of absolute mathematical truth. Most profoundly, however, quantum mechanics raised doubts on the notion of absolute objective reality. Is Schrödinger's cat dead or alive? Nearly 100 years after quantum mechanics was introduced, scientists still are not in full agreement on what it means.


Machine Learning: Speeding up ML with Quantum Computers

#artificialintelligence

Quantum Machine Learning is the use of Quantum Computers to do Machine Learning. The Machine Learning techniques applied often are "classical" or do not significantly differ from standard Machine Learning, although the algorithms may be implemented to be optimized for quantum computing. Sabre Kais, professor of chemical physics at Purdue, said that "this is an exciting time to combine machine learning with quantum computing. Impressive progress has been made recently in building quantum computers, and quantum machine learning techniques will become powerful tools for finding new patterns in big data." Jörg Esser, theoretical physicist, wrote that "Quantum computing enables exponential increases in speed by harnessing the weirdness of quantum mechanics. The key challenge is to build robust systems at scale."


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

#artificialintelligence

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.