A quantum binary classifier based on cosine similarity

• URL: http://arxiv.org/abs/2104.02975v1
• Date: Wed, 7 Apr 2021 07:55:49 GMT
• Title: A quantum binary classifier based on cosine similarity
• Authors: Davide Pastorello and Enrico Blanzieri
• Abstract summary: 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. We present a simple implementation of the considered classifier on the IBM quantum processor ibmq_16_melbourne.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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