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The quest for “quantum supremacy” – unambiguous proof that a quantum computer does something faster than an ordinary computer – has paradoxically led to a boom in quasi-quantum classical algorithms.
The fusion of quantum computing and machine learning has become a booming research area. Can it possibly live up to its high expectations?
Quantum computers should soon be able to beat classical computers at certain basic tasks. But before they’re truly powerful, researchers have to overcome a number of fundamental roadblocks.
To efficiently analyze a firehose of data, scientists first have to break big numbers into bits.
A new version of AlphaGo needed no human instruction to figure out how to clobber the best Go player in the world — itself.
The real-world version of the famous “traveling salesman problem” finally gets a good-enough solution.
A new idea is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.
A tiny self-organized mesh full of artificial synapses recalls its experiences and can solve simple problems. Its inventors hope it points the way to devices that match the brain’s energy-efficient computing prowess.