Hybrid Quantum-Classical Learning Models: A Survey of Emerging Paradigms

Abstract

The convergence of deep learning and quantum computing represents a significant shift in computational paradigms, offering the potential for enhanced processing power, improved learning efficiency, and greater scalability. While deep learning has achieved remarkable success in areas such as computer vision, natural language processing, and autonomous systems, it still faces challenges like long training times, high energy consumption, and the need for large datasets. Quantum computing, with its ability to perform parallel computations and tackle problems intractable for classical systems, presents a promising solution to these limitations. This survey explores the emerging field of Quantum Deep Learning (QDL), highlighting how quantum algorithms such as the Quantum Fourier Transform and Variational Quantum Circuits can accelerate the design and training of neural networks. We review current research developments, tools, and hybrid quantum-classical frameworks that integrate the strengths of both fields.

Additionally, we examine critical challenges, including quantum hardware noise, limited qubit availability, algorithm scalability, and the pressing need for robust quantum error correction. The paper also identifies future opportunities in areas such as quantum-enhanced optimization, drug discovery, financial modeling, and cryptography. This comprehensive survey aims to guide researchers and practitioners by outlining both the practical opportunities and the technical barriers in merging quantum computing with deep learning.