Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning

TL;DR

Neural Quantum Embedding (NQE) leverages classical deep learning to optimize quantum embeddings, significantly boosting classification accuracy, robustness, and trainability of quantum machine learning models on real quantum devices.

Contribution

NQE introduces a novel method that surpasses traditional quantum embedding limitations by integrating classical deep learning, improving performance and robustness in quantum machine learning.

Findings

Accuracy improved from 0.52 to 0.96 on IBM quantum devices.

NQE enhances robustness against noise.

Improves trainability and generalization of quantum neural networks.

Abstract

Quantum embedding is a fundamental prerequisite for applying quantum machine learning techniques to classical data, and has substantial impacts on performance outcomes. In this study, we present Neural Quantum Embedding (NQE), a method that efficiently optimizes quantum embedding beyond the limitations of positive and trace-preserving maps by leveraging classical deep learning techniques. NQE enhances the lower bound of the empirical risk, leading to substantial improvements in classification performance. Moreover, NQE improves robustness against noise. To validate the effectiveness of NQE, we conduct experiments on IBM quantum devices for image data classification, resulting in a remarkable accuracy enhancement from 0.52 to 0.96. In addition, numerical analyses highlight that NQE simultaneously improves the trainability and generalization performance of quantum neural networks, as well as of the quantum kernel method.