Quantum computing may be all the rage, but it turns out that the classic computers we use today may have some tricks up their sleeve.
We’re Close to a Universal Quantum Computer, Here’s Where We're At - https://youtu.be/6yaY4Fw-ovM
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Major Quantum Computing Advance Made Obsolete by Teenager
“The fast classical algorithm Tang found was directly inspired by the fast quantum algorithm Kerenidis and Prakash had found two years earlier. Tang showed that the kind of quantum sampling techniques they used in their algorithm could be replicated in a classical setting. Like Kerenidis and Prakash’s algorithm, Tang’s algorithm ran in polylogarithmic time — meaning the computational time scaled with the logarithm of characteristics like the number of users and products in the data set — and was exponentially faster than any previously known classical algorithm.”
A quantum-inspired classical algorithm for recommendation systems
“Our main result is an algorithm that samples high-weight entries from a low-rank approximation of the input matrix in time independent of m and n, given natural sampling assumptions on that input matrix. As a consequence, we show that Kerenidis and Prakash's quantum machine learning (QML) algorithm, one of the strongest candidates for provably exponential speedups in QML, does not in fact give an exponential speedup over classical algorithms..”
How Close Are We—Really—to Building a Quantum Computer?
““If it wasn’t complicated, we’d have one of these already,” says Jim Clarke, director of quantum hardware at Intel Labs (pdf). At the U.S. Consumer Electronics Show earlier this year, Intel introduced a 49-qubit processor code-named “Tangle Lake.” A few years ago the company created a virtual-testing environment for quantum-computing software; it leverages the powerful “Stampede” supercomputer (at The University of Texas at Austin) to simulate up to a 42-qubit processor.”
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