MIT boffin: Big data won't compute? Try these handy quantum algorithms Big Data? Check! Machine Learning? Check! Quantum computers? Check! Seth Lloyd, the self-dubbed "Quantum Mechanic", has ticked every box with a new (entirely theoretical) paper announcing a potential solution to problems unfeasible even before "the most powerful modern supercomputers". The paper, titled "Quantum algorithms for …
I think you will find that Seth Loyd doesn't NEED to justify his career. Especially to random AC retarded riff raff who couldn't pull a square root of -1 out of their arse if their life depended on it.
Still, this article is very confusing. It's a wall of words with no inherent meaning. And I am not fully unskilled in Quantum Computer Algorithms (for which I recommend this book, just saying)
I think you will find that Seth Loyd doesn't NEED to justify his career
And if he did, publishing work in his field in a refereed journal is how he's supposed to do it.
Still, this article is very confusing. It's a wall of words with no inherent meaning.
Well, yeah, it's the usual quick pop treatment of something that's not amenable to quick pop treatment.
The link for the paper given in the article has the abstract (and the whole paper, for that matter - no paywall), which helps. Key contribution appears to be a collection of quantum algorithms for finding parameters of topological features in a data set.
My guess, without having actually read the paper, is that the O(2N) complexity for conventional analysis comes from having to consider every subset of the points. In practice, I'd think you could often use graph sparsification and other techniques to derive a tractable problem that has a decent probability of producing useful results. But having another (actually useful) application for the long-promised many-qubit QC machines is good, I suppose. And a 300-qubit machine for doing topological analysis on a 300-point data set seems a bit more plausible than a 4096-qubit machine for cracking 4096-bit RSA keys.
I like the title "Quantum algorithms for topological and geometric analysis of data" it sounds to me a lot like the Tom Lehrer Song "Lobachevsky" is coming to life. :-)
............................
I am never forget the day I am given first original paper
to write. It was on analytic and algebraic topology of
locally Euclidean parameterization of infinitely differentiable
Riemannian manifold.
Bozhe moi!
This I know from nothing.
What-i'm going-to do.
But I think of great Lobachevsky and get idea - ahah!
I have a friend in Minsk,
Who has a friend in Pinsk,
Whose friend in Omsk
Has friend in Tomsk
With friend in Akmolinsk.
His friend in Alexandrovsk
Has friend in Petropavlovsk,
Whose friend somehow
Is solving now
The problem in Dnepropetrovsk.
And when his work is done -
Ha ha! - begins the fun.
From Dnepropetrovsk
To Petropavlovsk,
By way of Iliysk,
And Novorossiysk,
To Alexandrovsk to Akmolinsk
To Tomsk to Omsk
To Pinsk to Minsk
To me the news will run,
Yes, to me the news will run!
And then I write
By morning, night,
And afternoon,
And pretty soon
My name in Dnepropetrovsk is cursed,
When he finds out I publish first!
..........
The Register 
Biting the hand that feeds IT
Copyright. All rights reserved © 1998–2024
