Generalized coherent states, reproducing kernels, and quantum support vector machines

Rupak Chatterjee, Ting Yu

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

The support vector machine (SVM) is a popular machine learning classification method which produces a nonlinear decision boundary in a feature space by constructing linear boundaries in a transformed Hilbert space. It is well known that these algorithms when executed on a classical computer do not scale well with the size of the feature space both in terms of data points and dimensionality. One of the most significant limitations of classical algorithms using non-linear kernels is that the kernel function has to be evaluated for all pairs of input feature vectors which themselves may be of substantially high dimension. This can lead to computationally excessive times during training and during the prediction process for a new data point. Here, we propose using both canonical and generalized coherent states to calculate specific nonlinear kernel functions. The key link will be the reproducing kernel Hilbert space (RKHS) property for SVMs that naturally arise from canonical and generalized coherent states. Specifically, we discuss the evaluation of radial kernels through a positive operator valued measure (POVM) on a quantum optical system based on canonical coherent states. A similar procedure may also lead to calculations of kernels not usually used in classical algorithms such as those arising from generalized coherent states.

Original languageEnglish
Pages (from-to)1292-1306
Number of pages15
JournalQuantum Information and Computation
Volume17
Issue number15-16
StatePublished - Dec 2017

Keywords

  • Generalized coherent states
  • Quantum machine learning
  • Reproducing kernel hilbert spaces

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