A Problem That Only Quantum Computers Will Ever Be Able to Solve
quantamagazine.org
quantamagazine.org
> Since (despite my journalist moratorium) a journalist already emailed to ask me about the practical implications of the BQP vs. PH breakthrough—for example, for the ~70-qubit quantum computers that Google and others hope to build in the near future—let me take the opportunity to say that, as far as I can see, there aren’t any.
My first thought was "Oh, I have forgotten the address of that nice blog of that Prof. with all kind of interesting CS stuff that shows 'NP' and 'BQP' and 'P' connected with lines in the right upper corner. That blog will likely have more useful insights. How to find that blog again? I have so many bookmarks and not all tagged correctly, hmm".
But after reading the article and coming back here there is the right link near the top! :-)
People on HN are so great, love that! (Especially when they think in the same direction as me^^).
Please correct me if I'm wrong.
P means solvable in polynomial time relative to the size of the problem, which could still take longer than the universe has existed
Consider for example a randomized quicksort (O(n^2) worst case) is often faster than say mergesort (O(nlog(n)) worst case)) for small lists due to reduced overhead. I know these are both polynomial, but relatively speaking, randomized quicksort can be more efficient & quick.
In the real world we can make certain assumptions about our problem domain, where the most 'efficient' solution for your business problem may not have the smallest asymptotic time complexity.
Maybe it's fair to assume everyone reading this article knows what the auther means & i'm just being that guy, but I still don't like ambiguity lol
> "A probabilistic Turing machine can efficiently simulate any realistic model of computation." The word 'efficiently' here means up to polynomial-time reductions.
Real-world efficiency doesn't necessarily factor into what theoretical computer scientists are interested in.
[1] https://en.wikipedia.org/wiki/Church%E2%80%93Turing_thesis
>So instead, computer scientists measure something else that they hope will provide insight into the computation times they can’t measure: They work out the number of times a computer needs to consult an “oracle” in order to come back with an answer. An oracle is like a hint-giver. You don’t know how it comes up with its hints, but you do know they’re reliable.
This is not how it works, at all.
2. You don't work off number of oracle calls, but number of total steps with an oracle call counting as one step.
I don't know much about this subject, so I'm assuming one of my assumptions is wrong.
The simulation gets exponentially slow, to the point that the fastest classical computers we have are not practical for simulating even modestly sized quantum systems.
You can see this in the case of a basic quantum gate, the hadamard gate:
H(a) produces a qubit with equal probably of being 1 or 0 if observed; H(H(a)) will always return the original value of A. Doing this to an entangled vector of qubits, performing another transform, then hadamard again produces a quantum circuit that takes an impractical number of classical bits to simulate.
Imagine you have two random number generators, each
producing a sequence of digits. The question for your
computer is this: Are the two sequences completely
independent from each other, or are they related in a
hidden way?
That, right there, should tell absolutely everyone, by intuition alone, that, despite assurances from industry experts that flaws leading to breaks (plain-text discovery faster than brute force) are universally impractical, even with all the energy of a dyson sphere, that there are classified equations for back doors baked into all modern, commercially used civilian/consumer-grade cryptographic algorithms.I'll be here every night, ladies and gents.