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001     904616
005     20240403082755.0
024 7 _ |a 10.1038/s41928-021-00620-x
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100 1 _ |a Strachan, John Paul
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245 _ _ |a Fast Ising solvers based on oscillator networks
260 _ _ |a London
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520 _ _ |a Some of the hardest problems we throw at computers are known as NP-hard or NP-complete. In the worst-case scenarios, solving them requires an amount of time or compute resources that scales exponentially with problem size. These types of problem are ubiquitous, and can show up, for example, when deciding a shipping route for package deliveries, when wiring up a state-of-the-art computer chip, when figuring out how to parse DNA sequence data, and when training an artificial neural network. Today, we get around such problems using approximations that can be highly problem specific — or, in fact, just declaring the problem intractable. Fortunately, real-world problems often end up being considerably easier than the worst-case scenarios. Nonetheless, many industries anxiously seek more powerful computers to let them solve larger problems faster and with better quality results.
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773 _ _ |a 10.1038/s41928-021-00620-x
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|t Nature electronics
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856 4 _ |u https://juser.fz-juelich.de/record/904616/files/s41928-021-00620-x.pdf
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