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Quantum Machine Learning
Limitations of Clustering Using Quantum Persistent Homology
arXiv
Authors: Niels Neumann, Sterre den Breeijen
Year
2019
Paper ID
14582
Status
Preprint
Abstract Read
~2 min
Abstract Words
100
Citations
N/A
Abstract
Different algorithms can be used for clustering purposes with data sets. On of these algorithms, uses topological features extracted from the data set to base the clusters on. The complexity of this algorithm is however exponential in the number of data points. Recently a quantum algorithm was proposed by Lloyd Garnerone and Zanardi with claimed polynomial complexity, hence an exponential improved over classical algorithms. However, we show that this algorithm in general cannot be used to compute these topological features in any dimension but the zeroth. We also give pointers on how to still use the algorithm for clustering purposes.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2019 reference point for readers tracking recent quantum research.
- Different algorithms can be used for clustering purposes with data sets.
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