Quantum-Train with Tensor Network Mapping Model and Distributed Circuit Ansatz

TL;DR

This paper introduces a scalable hybrid quantum-classical machine learning framework that replaces traditional neural networks with tensor networks and employs a distributed circuit ansatz, improving efficiency and scalability.

Contribution

It proposes a novel tensor network-based model and distributed circuit ansatz within the Quantum-Train framework, enhancing scalability and interpretability for large-scale quantum machine learning.

Findings

Achieves competitive performance on benchmark datasets

Improves efficiency and generalization of quantum-classical models

Reduces parameter count and maintains inference independence from quantum resources

Abstract

In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of hybrid quantum-classical models. The traditional QT framework employs a multi-layer perceptron (MLP) for this task, but it struggles with scalability and interpretability. To address these issues, we propose replacing the MLP with a tensor network-based model and introducing a distributed circuit ansatz designed for large-scale quantum machine learning with multiple small quantum processing unit nodes. This approach enhances scalability, efficiently represents high-dimensional data, and maintains a compact model structure. Our enhanced QT framework retains the benefits of reduced parameter count and independence from quantum resources during inference. Experimental results on benchmark datasets demonstrate that the tensor network-based QT framework achieves competitive performance with improved efficiency and generalization, offering a practical solution for scalable hybrid quantum-classical machine learning.