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When an American college student showed that quantum computers have less of an advantage in recommendation systems than previously thought by developing a new algorithm that can run on classical computers, the response was overwhelming.
quantamagazine
University of Texas
Paper
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Student Develops Classical Algorithm for Recommendation Systems and Receives National Attention
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Two is not equal to three, even for large values of two.
(Score: 3, Interesting) by darkfeline on Tuesday August 07 2018, @07:13PM (1 child)
The paper is way too math heavy for me to skim casually, but there is no actual science here. I find it dubious that this would actually perform better in recommendation systems than machine learning,
As an algorithm/mathematical paper, I don't doubt that it is amazing, but I'm not sure of its practical usefulness.
https://users.ece.utexas.edu/~adnan/pike.html [utexas.edu]
Rob Pike's 5 Rules of Programming
Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.
Rule 2. Measure. Don't tune for speed until you've measured, and even then don't unless one part of the code overwhelms the rest.
Rule 3. Fancy algorithms are slow when n is small, and n is usually small. Fancy algorithms have big constants. Until you know that n is frequently going to be big, don't get fancy. (Even if n does get big, use Rule 2 first.)
Rule 4. Fancy algorithms are buggier than simple ones, and they're much harder to implement. Use simple algorithms as well as simple data structures.
Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
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(Score: 2) by AthanasiusKircher on Tuesday August 07 2018, @11:59PM
Well, I'm not sure how this would play out in relation to machine learning. But I think that's missing the point here.
The quantum algorithm in question was one of the first that seemed to prove a significant advantage over any classical algorithm in a practical application. Whether or not it would be the best approach is beside the point. It's an argument about algorithmic efficiency in quantum vs. classical computing on a very general level.
Apparently this new algorithm here shows that the techniques of the quantum algorithm can be replicated in a classical one. Hence a major finding about quantum algorithms and their major potential advantages in practical problems is now made more suspect.