Quantum Classification

Abstract

In supervised learning, a general classification problem is defined as the assignment of labels to new data instances given a training set of already labelled (classified) data. In this section we introduce some quantum algorithms to make predictions on labels of previously unseen data instances: two examples of quantum distance based classifiers, a quantum versions of the k-nearest neighbors algorithm, and the quantum support vector machine. Moreover, we overview a quantum-inspired classifier that is an algorithm based on the quantum formalism but devised for classical machines.