Work

A hybrid quantum-classical classification model based on branching multi-scale entanglement renormalization ansatz

Year: 2024

Type: article

Abstract: Tensor networks are emerging architectures for implementing quantum classification models. The branching multi-scale entanglement renormalization ansatz (BMERA) is a tensor network known for its enhanced entanglement properties. This paper introduces a hybrid quantum-classical classification model based on BMERA and explores the correlation between circuit layout, expressiveness, and classification accuracy. Additionally, we present an autodifferentiation method for computing the cost function gradient, which serves as a viable option for other hybrid quantum-classical models. Through numerical experiments, we demonstrate the accuracy and robustness of our classification model in tasks such as image recognition and cluster excitation discrimination, offering a novel approach for designing quantum classification models.

Source: Scientific Reports

DOI: 10.1038/s41598-024-69384-6

Authors: Yan-Yan Hou, Jian Li, Tao Xu, Xin-Yu Liu

Institutions: Zaozhuang University, Beijing University of Posts and Telecommunications

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,590

Funder: Zaozhuang University