methods for solving $\mathsf{NP}$-completeproblems that involve relaxation to a minimum-energy state, such as spin glasses  and protein folding.  All of these methods are subject to the same pitfalls of local optima and potentially long relaxation times.  Protein folding is an interesting case, since it seems likely that proteins evolved specifically *not* to have local optima.  A protein that folded in unpredictable ways could place whatever organism relied on it at an adaptive disadvantage (although sometimes it happens anyway, as with prions).  However, this also means that if we engineered an artificial protein to represent a hard 3SAT instance, then there would be no particular reason for it to fold as quickly or reliably as do naturally occurring proteins.

Quantum Computing\[QUANTUM\]
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Outside of theoretical computer science, parallel computers are sometimes discussed as if they were fundamentally more powerful than serial computers.  But of course, anything that can be done with $10^{20}$ processors in time $T$ can also be done with one processor in time $10^{20}T$.  The same is true for DNA strands.  Admittedly, for some applications a constant factor of $10^{20}$ is not irrelevant.[^6]  But for solving (say) 3SAT instances with hundreds of thousands of variables, $10^{20}$ is peanuts.

When 