Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines.
Jonas JägerRoman V. KremsPublished in: CoRR (2022)
Keyphrases
- high dimensional
- support vector
- kernel function
- large margin classifiers
- learning machines
- feature space
- svm classifier
- kernel methods
- feature selection
- quantum computing
- gaussian kernels
- quantum computation
- support vector machine
- quantum inspired
- logistic regression
- radial basis function
- svm classification
- rbf kernel
- loss function
- multiple kernel learning
- multi class classification
- binary classification
- generalization ability
- hyperplane
- structural risk minimization
- kernel classifiers
- cross validation
- maximum margin
- feature set
- multi class
- feature vectors
- polynomial kernels
- generalization bounds
- training set
- quantum mechanics
- margin maximization
- kernel logistic regression
- linear svm
- kernel machines
- reproducing kernel hilbert space
- support vector regression
- training examples
- naive bayes
- linear combination
- model selection
- optical flow