Local surrogates for quantum machine learning

TL;DR

This paper introduces local quantum surrogates that mimic quantum models for efficient inference, reducing quantum resource costs by focusing on data subregions and enabling classical dequantization.

Contribution

It proposes a novel local surrogation protocol using reuploading quantum models, enhancing efficiency and interpretability in quantum machine learning.

Findings

Local surrogates reduce qubit costs for targeted data regions.

Classical surrogates enable dequantization of inference phase.

Numerical experiments demonstrate effectiveness of the approach.

Abstract

Surrogates have been proposed as classical simulations of the pretrained quantum learning models, which are capable of mimicking the input-output relation inherent in the quantum model. Quantum hardware within this framework is used for training and for generating the classical surrogates. Inference is relegated to the classical surrogate, hence alleviating the extra quantum computational cost once training is done. Taking inspiration from interpretable models, we introduce a local surrogation protocol based on reuploading-type quantum learning models, including local quantum surrogates as cost-efficient intermediate quantum learning models. When the training and inference are only concerned with a subregion of the data space, deploying a local quantum surrogate offers qubit cost reductions and the downstream local classical surrogate achieves dequantization of the inference phase. Several numerical experiments are presented.