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You do forget that the final result is a hybrid classical-quantum computer. And certain applications like ML and quantum chemistry do allow a 1% error rate eas

You do forget that the final result is a hybrid classical-quantum computer. And certain applications like ML and quantum chemistry do allow a 1% error rate easy. The simulated annealing is the actual quantum chemistry, as are the direct quantum simulations in these chips. And much like any basic research, there's just no way to predict what might happen. New solid state batteries are a good guess, interesting materials, fuel cells? Potentially also improving on the reactor designs too. RCS is used as a benchmark because it is slower on classical by so many orders of magnitude. But it's a useful thing, literally made for chip testing, opening gates (soc) to higher yields. Then ultimately if it's big enough, it will start cracking classically and public key encryption forcing everyone to at least vastly upsize the keys, if not use resistant encryption.

thrance 2y ago
But that's 1% per instruction. It compounds, exponentially. Can ML and quantum chemistry take a 99.5% error rate? Bacause with 1000 gates deep circuits that's what they'll get. I can't talk about chemistry, but I've studied those quantum ML algorithms. I even designed one, a variant of the QSVM that I tested on IBM's computers when they were state of the art. They're all useless. There's just no way they'll compete with clusters of GPU in this century, I'd bet my hands on it. First you'd need good hardware that doesn't just output noise for long enough circuits to do something interesting. Second you'd need computers fast enough to be able to run many experiments one after the other, so you can do actual training (Alas, speed is antithetical to adiabatic operations). Third, you'd need to prove your quantum AI actually has an edge over classical ones to justify cooling down this much hardware to 0.01K for months at a time. I simply don't buy it.

thrance 2y ago

But that's 1% per instruction. It compounds, exponentially. Can ML and quantum chemistry take a 99.5% error rate? Bacause with 1000 gates deep circuits that's what they'll get. I can't talk about chemistry, but I've studied those quantum ML algorithms. I even designed one, a variant of the QSVM that I tested on IBM's computers when they were state of the art. They're all useless. There's just no way they'll compete with clusters of GPU in this century, I'd bet my hands on it. First you'd need good hardware that doesn't just output noise for long enough circuits to do something interesting. Second you'd need computers fast enough to be able to run many experiments one after the other, so you can do actual training (Alas, speed is antithetical to adiabatic operations). Third, you'd need to prove your quantum AI actually has an edge over classical ones to justify cooling down this much hardware to 0.01K for months at a time. I simply don't buy it.