  Recent technological breakthroughs have precipitated the availability of
specialized devices that promise to solve NP-Hard problems faster than standard
computers. These `Ising Machines' are however analog in nature and as such
inevitably have implementation errors. We find that their success probability
decays exponentially with problem size for a fixed error level, and we derive a
sufficient scaling law for the error in order to maintain a fixed success
probability. We corroborate our results with experiment and numerical
simulations and discuss the practical implications of our findings.
