Deep sequence models are increasingly combined with quantum computing and quantum-inspired optimization, yet the resulting literature uses inconsistent terminology and reports uneven evidence. This concise review distinguishes quantum-enhanced models, which execute parameterized quantum circuits, from quantum-inspired methods implemented on classical hardware. It organizes recent work by the point at which quantum principles enter the learning pipeline: recurrent gates, attention and feature maps, kernel representations, and training optimizers. Quantum long short-term memory networks and related hybrid architectures demonstrate expressive low-parameter models on small forecasting and classification tasks, while emerging superposition- and tunnelling-inspired optimizers target difficult neural-network loss landscapes. However, most reported gains are based on simulators, limited datasets, and comparisons that do not control computational budgets. The review therefore proposes a compact evaluation framework emphasizing strong classical baselines, wall-clock and energy costs, repeated trials, noise-aware experiments, and transparent definitions of quantum advantage. These practices are necessary to determine whether quantum mechanisms provide practical value for recurrent networks, Transformers, and future large-scale sequence models.
Deep sequence models are increasingly combined with quantum computing and quantum-inspired optimization, yet the resulting literature uses inconsistent terminology and reports uneven evidence. This concise review distinguishes quantum-enhanced models, which execute parameterized quantum circuits, from quantum-inspired methods implemented on classical hardware. It organizes recent work by the point at which quantum principles enter the learning pipeline: recurrent gates, attention and feature maps, kernel representations, and training optimizers. Quantum long short-term memory networks and related hybrid architectures demonstrate expressive low-parameter models on small forecasting and classification tasks, while emerging superposition- and tunnelling-inspired optimizers target difficult neural-network loss landscapes. However, most reported gains are based on simulators, limited datasets, and comparisons that do not control computational budgets. The review therefore proposes a compact evaluation framework emphasizing strong classical baselines, wall-clock and energy costs, repeated trials, noise-aware experiments, and transparent definitions of quantum advantage. These practices are necessary to determine whether quantum mechanisms provide practical value for recurrent networks, Transformers, and future large-scale sequence models.
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalization, and scalability, can be enhanced based on specific resource constraints. Distinct from broader quantum machine learning, QDL emphasizes compositional depth at the pipeline level and the integration of quantum or quantum-inspired components within end-to-end workflows. This review provides an operational definition of QDL and introduces a taxonomy comprising four primary paradigms: hybrid quantum-classical models, quantum deep neural networks, quantum algorithms for deep learning primitives, and quantum-inspired classical algorithms. Theoretical principles are connected to advanced architectures, software toolchains, and experimental demonstrations across superconducting, trapped-ion, photonic, semiconductor spin, and neutral-atom systems, as well as quantum annealers. Claims of quantum advantage are critically assessed by distinguishing provable complexity-theoretic separations from empirical observations. The analysis characterizes trade-offs between model expressivity, trainability, and classical simulability, while systematically detailing the bottlenecks imposed by optimization landscapes, input-output access models, and hardware constraints. Applications are surveyed in domains encompassing image classification, natural language processing, scientific discovery, quantum data processing, and quantum optimal control, underscoring fair benchmarking against optimized classical counterparts and a comprehensive assessment of resource requirements. This review serves as a tutorial entry point for graduate students while guiding readers to specialized literature. It concludes with a verification-aware roadmap to transition QDL from near-term demonstrations to scalable and fault-tolerant implementations.
Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalization, and scalability, can be enhanced based on specific resource constraints. Distinct from broader quantum machine learning, QDL emphasizes compositional depth at the pipeline level and the integration of quantum or quantum-inspired components within end-to-end workflows. This review provides an operational definition of QDL and introduces a taxonomy comprising four primary paradigms: hybrid quantum-classical models, quantum deep neural networks, quantum algorithms for deep learning primitives, and quantum-inspired classical algorithms. Theoretical principles are connected to advanced architectures, software toolchains, and experimental demonstrations across superconducting, trapped-ion, photonic, semiconductor spin, and neutral-atom systems, as well as quantum annealers. Claims of quantum advantage are critically assessed by distinguishing provable complexity-theoretic separations from empirical observations. The analysis characterizes trade-offs between model expressivity, trainability, and classical simulability, while systematically detailing the bottlenecks imposed by optimization landscapes, input-output access models, and hardware constraints. Applications are surveyed in domains encompassing image classification, natural language processing, scientific discovery, quantum data processing, and quantum optimal control, underscoring fair benchmarking against optimized classical counterparts and a comprehensive assessment of resource requirements. This review serves as a tutorial entry point for graduate students while guiding readers to specialized literature. It concludes with a verification-aware roadmap to transition QDL from near-term demonstrations to scalable and fault-tolerant implementations.
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.
Quantum computing and artificial intelligence (AI) are converging into a distinct research frontier commonly referred to as quantum-enhanced artificial intelligence, or quantum machine learning (QML). This paper presents a conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning. Using a structured narrative-review methodology, the study synthesizes theoretical foundations, algorithmic building blocks (quantum feature maps, variational quantum circuits, quantum kernel methods), and application domains spanning drug discovery, finance, materials science, and natural language processing. The review develops a hybrid quantum-classical architecture model and a complexity-comparison framework contrasting classical algorithms with their quantum counterparts, including Grover's search and Shor's factoring algorithm. Findings indicate that while theoretical speedups are well established, practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware remains constrained by decoherence, barren plateaus, and limited qubit connectivity. The paper contributes a synthesized taxonomy of quantum-enhanced AI methods and an evidence-based research agenda emphasizing error mitigation, hardware-aware ansatz design, and hybrid workload partitioning. The discussion further situates these developments within the broader trajectory of next-generation computing, arguing that near-term value will accrue primarily through hybrid quantum-classical systems rather than fully quantum pipelines. Implications for researchers, industry practitioners, and policymakers are discussed, alongside limitations inherent to a literature-synthesis approach.
The rapid growth of ultra-large and highdimensional datasets has intensified the demand for deep learning (DL) models that are scalable, accurate, transparent, and interpretable. In parallel, quantum-inspired computing has emerged as a promising paradigm that emulates quantum principles such as superposition, entanglement, and probabilistic state evolution without requiring quantum hardware. This study presents a comprehensive survey of explainable quantum-inspired DL models for large-scale data mining. Existing quantum-inspired architectures integrated with deep neural networks (DNN), including convolutional, recurrent, and attention-based models, are systematically analyzed alongside explainable artificial intelligence (XAI) techniques to enhance interpretability and trustworthiness. The reviewed methods are classified based on learning paradigms, explainability mechanisms, scalability strategies, and application domains such as healthcare analytics, cybersecurity, intelligent transportation, and industrial big data systems. Furthermore, evaluation metrics, computational complexity, and performance trade-offs are examined in comparison with conventional DL approaches. Finally, key open challenges and future research directions for real-world large-scale deployment are highlighted.
Quantum Neural Networks (QNNs) represent a compelling convergence of quantum mechanics and machine learning, yet their practical deployment remains constrained by noise, limited qubit counts, and the cost of full quantum execution. Hybrid quantum-classical (HQC) training offers a pragmatic path forward: parameterized quantum circuits (PQCs) process information in exponentially large Hilbert spaces while classical optimizers update parameters via gradient methods accessible on today's hardware. We present a modular HQC training framework, formalising the parameter-shift rule for hardware-compatible gradient estimation, characterising the barren-plateau phenomenon through a variance-decay analysis, and introducing noise-aware training strategies for NISQ devices. Empirical results from state-vector and shot-based simulation confirm measurable quantum advantage on quantum-structured tasks, with the phase classification benchmark improving +12.1% over a classical MLP baseline. The accompanying open-source library provides a clean abstraction between quantum simulation and classical optimisation, serving as a foundation for near-term quantum machine learning research.