Quantum computers get a lot of hype, but the truth is we’re still not sure what they’ll be good for. These devices leverage the peculiar physics of the subatomic world and have the potential to perform calculations that regular, classical computers simply can’t. But it’s proved difficult to find examples of any algorithms with a clear “quantum advantage” that enables performance beyond the reach of classical machines.
For most of the 2010s, many computer scientists felt one particular group of applications had a great shot at finding this advantage. Certain data-analysis calculations would be exponentially faster when they were crunched by a quantum computer.
Then along came Ewin Tang. As an 18-year-old recent college grad in 2018, she found a new way for classical computers to solve these problems, smacking down the advantage the quantum algorithms had promised. For many who work on quantum computers, Tang’s work was a reckoning. “One by one, these super exciting use cases just got killed off,” said Chris Cade, a theoretical computer scientist at the Dutch quantum computing research center QuSoft.
But one algorithm survived unscathed: a quantum twist on a niche mathematical approach for studying the “shape” of data, called topological data analysis (TDA). After a flurry of papers in September, researchers now believe that these TDA calculations lie beyond the grasp of classical computers, perhaps due to a hidden connection to quantum physics. But this quantum advantage may only occur under highly specific conditions, calling its practicality into question.
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