International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Hybrid Quantum Deep Learning Models: Architectures, Applications, and Research Outlook

Author(s) Dr. Partha Sarathi Das
Country India
Abstract Hybrid Quantum Deep Learning (HQDL) models have emerged as a pragmatic bridge between the algorithmic richness of deep learning and the computational advantages of near-term quantum devices. Rather than seeking full quantum replacement of neural architectures, current research favours layered or modular integration-where variational quantum circuits, quantum kernels, or quantum-enhanced feature embeddings operate alongside classical neural networks. This convergence has yielded promising results in domains such as classification, generative modelling, molecular simulation, and combinatorial optimization. However, performance varies significantly depending on data encoding strategies, circuit expressivity, and training stability under hardware noise. This review synthesizes the taxonomy of existing HQDL architectures, evaluates their empirical benefits over purely classical baselines, and contextualizes their feasibility within current Noisy Intermediate-Scale Quantum (NISQ) constraints. Furthermore, it highlights reproducibility challenges, benchmarking inconsistencies, and open questions surrounding optimization landscapes and scalability. The analysis concludes that while HQDL remains in an early experimental phase, its trajectory is rapidly transitioning from proof-of-concept demonstrations toward targeted domain specialization in finance, materials discovery, and strategic decision-making.
Keywords Hybrid Quantum Deep Learning; Variational Quantum Circuits; Quantum Neural Networks; Quantum-Classical Systems; NISQ Computing
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-27
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.58909
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E-ISSN 2582-2160
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