A Quantum Binary Classifier based on Cosine Similarity

被引：3
作者：
Pastorello, Davide [1 ,2 ]
Blanzieri, Enrico [1 ,2 ]
机构：
[1] Univ Trento, Dept Informat Engn & Comp Sci, Via Sommar 9, I-38123 Povo, TN, Italy
[2] TIFPA INFN, Via Sommar 14, I-38123 Povo, Trento, Italy
来源：
2021 IEEE INTERNATIONAL CONFERENCE ON QUANTUM COMPUTING AND ENGINEERING (QCE 2021) / QUANTUM WEEK 2021 | 2021年
关键词：
Quantum algorithms; quantum machine learning; binary classification; c osine similarity;
D O I：
10.1109/QCE52317.2021.00086
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
This proposal introduces the quantum implementation of a binary classifier based on cosine similarity between data vectors. The proposed quantum algorithm presents time complexity that is logarithmic in the product of the training 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, and a measurement process on a single qubit. An implementation on an IBM quantum processor is presented.
引用
收藏
页码：477 / 478
页数：2
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