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

Abstract

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.

Keywords

• Tensor networks
• Hybrid quantum-classical classification model
• Branching multi-scale entanglement renormalization ansatz (BMERA)
• Quantum machine learning