> Things like physical stimulation of quantum systems, protein folding, machine learning, etc. could be more useful

is there still more to do in protein folding after AlphaFold?

https://www.isomorphiclabs.com/articles/alphafold-3-predicts...

api 6 days ago | parent | next [-]

There’s a difference between good AI predictions and theoretically perfect QC computations. The AI estimates while the QC will give you the answer, full stop. The latter could be relied upon more strongly. It could also generate infinite training data to make much better models.

QC might be directly applicable to AI training too. It may be possible to compute the optimal model over a data set in linear time. It could allow training that is faster and consumes a tiny fraction of the energy current brute force methods need.

	▲	_delirium 6 days ago \| parent \| next [-]
		There have in fact been some results on quantum speedups for machine learning: https://www.quantamagazine.org/ai-gets-a-quantum-computing-s... I would expect this to become relevant later than crypto, though, because you need larger data sizes for things to get interesting.
	▲	tsimionescu 6 days ago \| parent \| prev [-]
		Is there any known quantum exponential speedup for gradient descent?

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dwroberts 6 days ago | parent | prev [-]

The predictions don't tell us anything about why the answer is what it is. There is probably important (useful) fundamental scientific knowledge in being able to know that vs. just being able to predict the result.