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вЂњTo my sense it was one of the first examples in machine learning and big data where we showed quantum computers can do something that we still donвЂ™t know how to do classically,вЂќ said Kerenidis, a computer scientist at the Research Institute on the Foundations of Computer Science in Paris. Kerenidis and PrakashвЂ™s result was exciting because it provided a real-world problem people cared about where quantum computers outperformed classical ones. Most of those examples were specialized - they were narrow problems designed to play to the strengths of quantum computers (these include the вЂњforrelationвЂќ problem Quanta covered earlier this year). They achieved this quantum speedup in part by simplifying the problem: Instead of filling out the entire matrix and identifying the single best product to recommend, they developed a way of sorting users into a small number of categories - do they like blockbusters or indie films? - and sampling the existing data in order to generate a recommendation that was simply good enough.ДЄt the time of Kerenidis and PrakashвЂ™s work, there were only a few examples of problems that quantum computers seemed to be able to solve exponentially faster than classical computers. In 2016 the computer scientists Iordanis Kerenidis and Anupam Prakash published a quantum algorithm that solved the recommendation problem exponentially faster than any known classical algorithm. A good algorithm would generate recommendations by quickly and accurately recognizing similarities between movies and users and filling in the blanks in the matrix. You can think of this data as being arranged in a giant grid, or matrix, with movies listed across the top, users listed down the side, and values at points in the grid quantifying whether, or to what extent, each user likes each film. Given this information, what are you likely to want to watch next? It knows what all of its other millions of users have watched. The recommendation problem is designed to give a recommendation for products that users will like. вЂњI was hesitant because it seemed like a hard problem when I looked at it, but it was the easiest of the problems he gave me,вЂќ Tang said. Aaronson gave Tang a handful of problems to choose from, including the recommendation problem. Aaronson recognized Tang as an unusually talented student and offered himself as adviser on an independent research project. In the spring of 2017 Tang took a class on quantum information taught by Scott Aaronson, a prominent researcher in quantum computing. In 2014, at age 14 and after skipping the fourth through sixth grades, Tang enrolled at UT Austin and majored in mathematics and computer science. at the University of Washington in the fall. вЂњThis was one of the most definitive examples of a quantum speedup, and itвЂ™s no longer there,вЂќ said Tang, who graduated from the University of Texas, Austin, in spring and will begin a Ph.D.

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