Quantum algorithms for topological and geometric analysis of big data · HackerPapers discussions

Quantum algorithms for topological and geometric analysis of big data — mentioned by johncolanduoni

Mon, 17 Oct 2016 20:51:38 GMT

While the OP is wrong about being able to "do everything in parallel" via quantum superposition (we have plenty of no-go theorems when it comes to quantum computers being faster simply due to parallelization) some interesting algorithms that are relevant to data science in general have been developed. For example, this[1] one provides an exponential speedup (over known classical algorithms) for calculating persistent homology, a useful method for identifying scale-independent patterns. [1]: https://arxiv.org/abs/1408.3106

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Quantum algorithms for topological and geometric analysis of big data — mentioned by GFK_of_xmaspast

Tue, 26 Jan 2016 03:09:25 GMT

The actual abstract from http://www.nature.com/ncomms/2016/160125/ncomms10138/abs/nco...: "Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers—the numbers of connected components, holes and voids—in persistent…

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Quantum algorithms for topological and geometric analysis of big data — mentioned by mathgenius

Sat, 11 Oct 2014 01:42:01 GMT

This one is good too: "Quantum algorithms for topological and geometric analysis of big data" http://arxiv.org/abs/1408.3106 It uses some of this "applied topology", which I only recently found out about. http://www.math.upenn.edu/~ghrist/notes.html

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