urgoroger

5 ms·

1.
by urgoroger 8y ago
What I was trying to communicate was that the Turing machine believed to represent the limits of what is physically possible. So then you have turing machines >= brain (since a brain is physical) and brain >= turing machine (by simul
2.
by urgoroger 8y ago
In accordance with the Church-Turing thesis, the Turing machine stands to be capable of doing anything that should be called computation. It follows that if the brain is capable of simulating of a Turing machine (this is called a universal
3.
by urgoroger 8y ago
I think the commentor's point is that measurement (which is a key component of quantum computation) is not a unitary operation.
4.
by urgoroger 8y ago
It might be easier to view entropy of a state as sort of the probability that that state would occur after randomly arranging all of its parts (usually particles). In fact this is pretty close to the actual definition. Here's an exampl
5.
by urgoroger 9y ago
Yes, this is pretty much the basis to quantum key distribution protocols, and probably one of the first studied uses of quantum information in general. Consider BB84 ( http://www.cse.wustl.edu/~jain/cse571-07/ftp&#x
6.
by urgoroger 9y ago
I want to add that quantum FFT performs the transform on the amplitudes in the n-qubit state. Thus its output is an n-qubit state with the amplitudes matching the fourier tranforms. We cannot actually directly extract the amplitudes of the
7.
by urgoroger 9y ago
I think you have the right idea but the wrong terminology. A n qubit system requires amplitudes (probabilities) for all 2^n possible basis states. For example, the representation we can give to a 2-qubit system is just a probability (techni
8.
by urgoroger 9y ago
That's correct. Given a polynomial time approximate algorithm for Ek-SAT (note, by approximate algorithm I mean along the lines of the formal definition of approximate algorithm, that the solution given by the algorithm falls in some f
9.
by urgoroger 9y ago
While I agree with you, you should note that even approximation within any degree of error is NP-complete for a large class of NP-complete problems (e.g. TSP, and the problem MAX-EkSAT used in the paper). That is, a polynomial algorithm for
10.
by urgoroger 9y ago
Don't worry! I study quantum computation, and I didn't notice that the |> was supposed to be a D to form the word LIQUID!
11.
by urgoroger 9y ago
Could be wrong too, ML is not my field. Basically, a generator neural network has two things which affect its output: an input and some parameters (weights). Let's use the setting of the task for denoising images. They use a network wh
12.
by urgoroger 9y ago
x is actually the generated image they are testing against x0. A lower E(x; x0) means an image which fits well towards the objective based on the original image (depends on the task). The paper gives some examples. For example, for the task
13.
by urgoroger 9y ago
This is correct, and I think it's mentioned in the article. I think this is probably what most people would think of too rather than the heap based solution. Using the heap solution also requires you to know how to use a heap in whatev
14.
by urgoroger 9y ago
There are a two cases: BQP contains something BPP doesn't: Then the thesis is false as long as we can realize a quantum computer, as then there exists something which can be solved in polynomial time (with bounded error) on a machine i
15.
by urgoroger 9y ago
While it is true that compatibility should remain intact, this comment seems to misunderstand what the STRONG Church-Turing thesis purports (EDIT: at least the one Google is probably referring to). I'm not sure if it's completely