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@ARTICLE{Strachan:904616,
author = {Strachan, John Paul},
title = {{F}ast {I}sing solvers based on oscillator networks},
journal = {Nature electronics},
volume = {4},
number = {7},
issn = {2520-1131},
address = {London},
publisher = {Springer Nature Limited},
reportid = {FZJ-2021-06186},
pages = {458 - 459},
year = {2021},
abstract = {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.},
cin = {PGI-14},
ddc = {621.3},
cid = {I:(DE-Juel1)PGI-14-20210412},
pnm = {5234 - Emerging NC Architectures (POF4-523)},
pid = {G:(DE-HGF)POF4-5234},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000677836800008},
doi = {10.1038/s41928-021-00620-x},
url = {https://juser.fz-juelich.de/record/904616},
}