Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning

TL;DR

This paper reviews quantum circuit-based machine learning models, exploring their potentials, challenges, and recent advancements in integrating quantum computing with classical ML for improved data analysis.

Contribution

It provides a comprehensive overview of quantum circuit-based models, including kernel and neural network approaches, and discusses their integration, theoretical analysis, and practical challenges.

Findings

Quantum models show promise in classical data analysis.

Hybrid quantum-classical frameworks enhance model capabilities.

Noise-resilient and hardware-efficient approaches are emerging.

Abstract

Machine Learning (ML) has been widely applied across numerous domains due to its ability to automatically identify informative patterns from data for various tasks. The availability of large-scale data and advanced computational power enables the development of sophisticated models and training strategies, leading to state-of-the-art performance, but it also introduces substantial challenges. Quantum Computing (QC), which exploits quantum mechanisms for computation, has attracted growing attention and significant global investment as it may address these challenges. Consequently, Quantum Machine Learning (QML), the integration of these two fields, has received increasing interest, with a notable rise in related studies in recent years. We are motivated to review these existing contributions regarding quantum circuit-based learning models for classical data analysis and highlight the identified potentials and challenges of this technique. Specifically, we focus not only on QML models, both kernel-based and neural network-based, but also on recent explorations of their integration with classical machine learning layers within hybrid frameworks. Moreover, we examine both theoretical analysis and empirical findings to better understand their capabilities, and we also discuss the efforts on noise-resilient and hardware-efficient QML that could enhance its practicality under current hardware limitations. In addition, we cover several emerging paradigms for advanced quantum circuit design and highlight the adaptability of QML across representative application domains. This study aims to provide an overview of the contributions made to bridge quantum computing and machine learning, offering insights and guidance to support its future development and pave the way for broader adoption in the coming years.