Quantum speed-up for unsupervised learning
We show how the quantum paradigm can be used to speed up unsupervised learning algorithms. More precisely, we explain how it is possible to accelerate learning algorithms by quantizing some of their subroutines. Quantization refers to the process that partially or totally converts a classical algori...
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| Veröffentlicht in: | Machine learning 2013-02, Vol.90 (2), p.261-287 |
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| container_title | Machine learning |
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| creator | Aïmeur, Esma Brassard, Gilles Gambs, Sébastien |
| description | We show how the quantum paradigm can be used to speed up unsupervised learning algorithms. More precisely, we explain how it is possible to accelerate learning algorithms by quantizing some of their subroutines. Quantization refers to the process that partially or totally converts a classical algorithm to its quantum counterpart in order to improve performance. In particular, we give quantized versions of clustering via minimum spanning tree, divisive clustering and
k
-medians that are faster than their classical analogues. We also describe a distributed version of
k
-medians that allows the participants to save on the global communication cost of the protocol compared to the classical version. Finally, we design quantum algorithms for the construction of a neighbourhood graph, outlier detection as well as smart initialization of the cluster centres. |
| doi_str_mv | 10.1007/s10994-012-5316-5 |
| format | Article |
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k
-medians that are faster than their classical analogues. We also describe a distributed version of
k
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k
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k
-medians that allows the participants to save on the global communication cost of the protocol compared to the classical version. Finally, we design quantum algorithms for the construction of a neighbourhood graph, outlier detection as well as smart initialization of the cluster centres.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial Intelligence</subject><subject>Classical and quantum physics: mechanics and fields</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Clusters</subject><subject>Computer Science</subject><subject>Computer science; control theory; systems</subject><subject>Construction costs</subject><subject>Control</subject><subject>Cost engineering</subject><subject>Counting</subject><subject>Cryptography and Security</subject><subject>Exact sciences and technology</subject><subject>Graph theory</subject><subject>Information retrieval. 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k
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k
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| subjects | Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial Intelligence Classical and quantum physics: mechanics and fields Cluster analysis Clustering Clusters Computer Science Computer science control theory systems Construction costs Control Cost engineering Counting Cryptography and Security Exact sciences and technology Graph theory Information retrieval. Graph Learning Mechatronics Natural Language Processing (NLP) Physics Quantum computation Quantum information Quantum theory Robotics Simulation and Modeling Theoretical computing |
| title | Quantum speed-up for unsupervised learning |
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