Who needs qubits? Factoring algorithm run on a probabilistic computer(arstechnica.com)
arstechnica.com
Who needs qubits? Factoring algorithm run on a probabilistic computer
https://arstechnica.com/science/2019/09/who-needs-qubits-factoring-algorithm-run-on-a-probabilistic-computer/
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I agree that the integer factorization is a poor choice. Last statement in the Abstract talks about sampling and optimization which could be the bigger point.
Their pre-processing seems like simply expanding out their cost function that's of the form E= (F - XY)^2. Of course it's a lot of multiplications since X and Y are binary and multi-dimensional. Not sure if it would be NP-hard though.
Their pre-processing seems like simply expanding out their cost function that's of the form E= (F - XY)^2. Of course it's a lot of multiplications since X and Y are binary and multi-dimensional. Not sure if it would be NP-hard though.
I thought the whole magic of quantum-computing was that instead of just having individual bits in an uncertain/random state, you entangled a whole bunch of bits together, meaning you can meaningfully talk about the probability distribution of an 8-bit value, that's different from 8 independent 1-bit distributions.
Then, you perform operations where the value interacts with alternate values for itself (i.e. the full wave-function) - a bit like the double-slit experiment. For example, you can end up with a new 8-bit value where the probability distribution is the Fourier transform of the previous one's distribution.
So, if you can engineer the initial probability distribution to be "interesting", you can then sample its Fourier transform - using only the 8-qubit values, and not storing 2^8-point distribution in an array. Scale that up, and you could calculate a useful 2048-bit Fourier transform (or more accurately: observe a random sample from the result) with a 2048-qubit system, instead of a 2^2048-point array.
It's not obvious to me how stochastically-changing bits of state can get anywhere close to self-interacting (double-slit-like) calculations.
Then, you perform operations where the value interacts with alternate values for itself (i.e. the full wave-function) - a bit like the double-slit experiment. For example, you can end up with a new 8-bit value where the probability distribution is the Fourier transform of the previous one's distribution.
So, if you can engineer the initial probability distribution to be "interesting", you can then sample its Fourier transform - using only the 8-qubit values, and not storing 2^8-point distribution in an array. Scale that up, and you could calculate a useful 2048-bit Fourier transform (or more accurately: observe a random sample from the result) with a 2048-qubit system, instead of a 2^2048-point array.
It's not obvious to me how stochastically-changing bits of state can get anywhere close to self-interacting (double-slit-like) calculations.
They used 8 p-bits and could factor numbers up to 950, which took about 15 seconds. They say "it's possible that a larger number of p-bits will mean a significantly larger sampling time." That seems likely to be a drastic understatement.
Funny that's what a human takes to factor 950 with pen and paper
This doesn't make sense to me (theoretical computer scientist). If I run the code
So you don't need any special hardware to do classical probabilistic computations. You just need a source of entropy and a standard computer.
Now it might be that a different architecture than the standard von Neumann processor+memory one could give a polynomial speedup for certain problems. And that would be fine. But the focus of this Ars article as well as the Nature letter abstract is on p-bits, which seems nonsensical to me.
(Added later) I see there is an emphasis on the bits fluctuating or evolving over time, annealing I guess. We could of course simulate this on a standard computer, but not as easily. Still I am reminded of Aaronson's soap bubbles...
x = rand() % 2
So you don't need any special hardware to do classical probabilistic computations. You just need a source of entropy and a standard computer.
Now it might be that a different architecture than the standard von Neumann processor+memory one could give a polynomial speedup for certain problems. And that would be fine. But the focus of this Ars article as well as the Nature letter abstract is on p-bits, which seems nonsensical to me.
(Added later) I see there is an emphasis on the bits fluctuating or evolving over time, annealing I guess. We could of course simulate this on a standard computer, but not as easily. Still I am reminded of Aaronson's soap bubbles...
If I understand correctly, the key difference is that the rand() function in a Turing machine cannot generate true random numbers (I think it's something like Martin-Lof?).
Pseudorandomness is related, but doesn't affect this issue. We can hook up a physically random process to a computer for a source of "true" random bits, then go from there.
x = rand() % 2 always gives a random number. You can't
correlate them that way.
Needs to be a tunable random number and technically needs to be "true" as mentioned.
But I don't think that the authors would disagree with your larger point - their point is to provide polynomial speed-up over digital computers for a certain class of problems. That this can be done with classical computers isn't exactly a deep insight, it's rather obvious.
Needs to be a tunable random number and technically needs to be "true" as mentioned.
But I don't think that the authors would disagree with your larger point - their point is to provide polynomial speed-up over digital computers for a certain class of problems. That this can be done with classical computers isn't exactly a deep insight, it's rather obvious.
This seems fundamentally flawed, because you can sample the solution space at most with a speed that is comparable to existing logic gates (and the interconnect is the main bottleneck).
Not sure why anybody would make the effort of researching this idea, or even why ars would publish an article on it.
Not sure why anybody would make the effort of researching this idea, or even why ars would publish an article on it.
What complexity class describes these computing devices?
PP implies that that computation runs for a polynomial number of steps before producing its probabilistic answer. In the article description, the computation seems to be run "until the answer is separated from the noise." And although they are hopeful, I don't think they are asserting at the moment that this will happen after a polynomial number of steps. So PP would not apply.
If it happens in a polynomial number of steps on average, but you might get unlucky, https://en.wikipedia.org/wiki/ZPP_(complexity)
If it can just take as long as they want, then the probabilistic part is basically irrelevant and you're probably looking at https://en.wikipedia.org/wiki/PSPACE
In any case, I very much doubt they'd made any real breakthrough in factoring numbers.
If it can just take as long as they want, then the probabilistic part is basically irrelevant and you're probably looking at https://en.wikipedia.org/wiki/PSPACE
In any case, I very much doubt they'd made any real breakthrough in factoring numbers.
Are they claiming that they have a computer that shows BPP = PP? That's absurd.
So I know nothing about probabilistic computing, but is my intuition about how these work correct?
Essentially, a qubit can have a superposition over 0 and 1 and operations upon that qubit will change the probability and then when you "pop it" to read it out you will get the output that you want depending on the program that you set up.
By contrast, this "pbit" works by having some probability between 0 and 1 and this is represented by something, for example a time varying flicker between 0 and 1 which is weighted by the same aforementioned probability. Then, operations upon the pbit can change the probability depending upon the program and you read it out by sampling to see what the output of it is supposed to be.
If so, wouldn't it suffer from the Monte Carlo integration issue? Namely, in theory it is nearly independent of the number of dimensions, but in practice as your number of dimensions (here the number of bits, pbits or qubits) goes up you will get worse results?
Essentially, a qubit can have a superposition over 0 and 1 and operations upon that qubit will change the probability and then when you "pop it" to read it out you will get the output that you want depending on the program that you set up.
By contrast, this "pbit" works by having some probability between 0 and 1 and this is represented by something, for example a time varying flicker between 0 and 1 which is weighted by the same aforementioned probability. Then, operations upon the pbit can change the probability depending upon the program and you read it out by sampling to see what the output of it is supposed to be.
If so, wouldn't it suffer from the Monte Carlo integration issue? Namely, in theory it is nearly independent of the number of dimensions, but in practice as your number of dimensions (here the number of bits, pbits or qubits) goes up you will get worse results?
Its going to be much cheaper because all the cooling, error correction and other quantum interface stuff is not needed - its just a bunch of magnetic memory cells. Lets see how it scales.
It seems to me that this is a kind of monte carlo implementation of fuzzy logic.
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Skimming over the paper it seems their method of translating factorization to the optimization problem consists of simplifying equations without justification that this can be done efficiently. I suspect that their preprocessing step is already NP-hard.
The second and more important issue, is that the overall strategy---of translating a problem with a sub-exponential classical complexity (via the Number Field Sieve) to an optimization problem with exponential runtime---is not expected to succeed, as confirmed by careful measurements in our paper.