Supervised Discriminative Classification in Quantum Machine Learning
- Middlesex University London
Thursday, September 28, 2017
Quantum Machine Learning is a recent area of research initiated by the demonstrations of quantised variants of standard machine learning algorithms such as the Quantum Support Vector Machine (SVM) by Rebentrost, Mohseni & Lloyd and the Quantum K-Means algorithm of Aïmeur, Brassard and Gambs. The development of the quantum SVM can be regarded as particularly significant in that the classical SVM constitutes the exemplar instance of a supervised binary classifier, i.e. an entity capable of learning an optimal discriminative decision hyperplane from labeled vectors. We explore this classifier in detail along with its Kernelised variants, as well as investigating an ensemble-based enhancement to enable variance-resilient quantum machine learning.
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