
Combining topology and quantum computing = huge analysis of data sets - quackerhacker
http://www.pcworld.com/article/3026228/what-happens-when-big-data-gets-too-big-quantum-computers-may-hold-the-key.html
======
GFK_of_xmaspast
The actual abstract from
[http://www.nature.com/ncomms/2016/160125/ncomms10138/abs/nco...](http://www.nature.com/ncomms/2016/160125/ncomms10138/abs/ncomms10138.html):
"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 homology, and for finding
eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms
provide an exponential speed-up over the best currently known classical
algorithms for topological data analysis."

Arxiv version here:
[http://arxiv.org/abs/1408.3106](http://arxiv.org/abs/1408.3106)

