A quantum binary classifier based on cosine similarity

TL;DR

This paper presents a quantum binary classifier utilizing cosine similarity, achieving logarithmic time complexity and demonstrating implementation on IBM quantum hardware, with potential integration into hybrid quantum-classical algorithms.

Contribution

The paper introduces a novel quantum binary classifier based on cosine similarity, with efficient implementation and integration into hybrid quantum-classical frameworks.

Findings

Logarithmic time complexity in data set size and dimension

Successful implementation on IBM quantum processor

Potential for integration with quantum K-nearest neighbors

Abstract

We introduce the quantum implementation of a binary classifier based on cosine similarity between data vectors. The proposed quantum algorithm evaluates the classifier on a set of data vectors with time complexity that is logarithmic in the product of the set cardinality and the dimension of the vectors. It is based just on a suitable state preparation like the retrieval from a QRAM, a SWAP test circuit (two Hadamard gates and one Fredkin gate), and a measurement process on a single qubit. Furthermore we present a simple implementation of the considered classifier on the IBM quantum processor ibmq_16_melbourne. Finally we describe the combination of the classifier with the quantum version of a K-nearest neighbors algorithm within a hybrid quantum-classical structure.