D-Wave Open Sources Its Quantum Computing Tool (gcn.com)
Long-time Slashdot reader haruchai writes: Canadian company D-Wave has released their qbsolv tool on GitHub to help bolster interest and familiarity with quantum computing. "qbsolv is a metaheuristic or partitioning solver that solves a potentially large QUBO problem by splitting it into pieces that are solved either on a D-Wave system or via a classical tabu solver," they write on GitHub.
This joins the QMASM macro assembler for D-Wave systems, a tool written in Python by Scott Pakin of Los Alamos National Labs. D-Wave president Bo Ewald says "D-Wave is driving the hardware forward but we need more smart people thinking about applications, and another set thinking about software tools."
This joins the QMASM macro assembler for D-Wave systems, a tool written in Python by Scott Pakin of Los Alamos National Labs. D-Wave president Bo Ewald says "D-Wave is driving the hardware forward but we need more smart people thinking about applications, and another set thinking about software tools."
french toast, ladies
"qbsolv is a metaheuristic or partitioning solver that solves a potentially large QUBO problem by splitting it into pieces that are solved either on a D-Wave system or via a classical tabu solver"
I know some of those words but all I can really tell is that it apparently does things to stuff, or does stuff to things.
Just cruising through this digital world at 33 1/3 rpm...
... any faster than can be done on conventional computers, today. All this hype around the D-Wave machines should not distract us from the fact that when competing for solving a given problem fastest against conventional computers, using the algorithms best suited for the respective hardware (and not making the conventional computer simulate a D-Wave like machine), the D-Wave machine loses every contest.
Open-sourcing some tools won't change that in any way.
Take any process which can be simulated by a computer, and create optimized hardware to run that simulation. Encryption, 3D video, "quantum computing". Optimized hardware can outperform any general-purpose computer, even at lower clock speeds, especially if the operations don't map well to what is considered general-purpose. How much of D-Wave's positive reception is due to "ground-breaking quantum computing progress" vs. actual improvements in computation? I'm not saying that they haven't made real progress in running the algorithms quantum computers are claimed to do so well, I'm just saying there's no proof that any "quantum computing" is actually going on. While it isn't necessarily easy to do the faking, it's easy to cover it up when you don't let anybody look at the low-level hardware. While I expect the government to buy into this crap (they did, after all, fund psychic experiments for decades), I wonder what Google gets out of it.
"we need more smart people thinking about applications" = we have a solution looking for a problem
D-wave is not quantum computing. It's regular, non-quantum computing that uses software to simulate what we think using non-locality in computing would be.
Humans actually controlling quantum non-locality would be arguably the biggest feat since harnessing the atom...it amazes me how this blatant bs continues to be called "quantum"...
Thank you Dave Raggett
... compared to a normal computer or a GPU? And what about the energy consumption? Is it useful more than the coolness of the quantum thing?
People seem to say that as if it were a bad thing. Personally I'd rather have solutions to problems I've yet to have. Inversely, I wouldn't mind having all of my future problems solved using only existing solutions. A win-win in my book.
We have a hammer that does interesting things when swung. We want to study this hammer to see if what we learn can't be used to make a hammer that is actually better than the best hammers out there.
I'm disappointed to find it splits up the problem, you rarely find an optimal solution by trying each variable as though they're independent.
And I'm most disappointed that it's unconstrained. Which makes it totally useless for every problem I've ever used an optimizer.
CMAES (available in various libraries, e.g. Apache Math) is probably a better choice for "can't solve it any other way" optimizations. That tries N points randomly distributed across each variable depending on each variables deviation. With N based on solid probability, ( but still may not find the perfect solution).
You'd think that they wouldn't have to split it up, with 'quantum fakentanglement' linking the variables at faster than the speed of light... but then what do I know. I only know statistics for data mining, common faults in datamining (like prefiltering for linking variables on apparently uncorrelated data), montecarlo tests and so on.... just math and science not quantum magic.