Research on the application potential and synergistic effects of quantum computing in the field of artificial intelligence: Integrative analysis centered on quantum machine learning (VQC/QNN)
The rapid development of deep learning and large language models (LMs) has led to a "computing inflation" phenomenon in the demand for computing power, energy consumption, and training time for artificial intelligence (AI). As classical computing gradually approaches its scalability bottleneck due to the slowing of Moore's Law and limitations imposed by memory walls and energy consumption, quantum computing (QC), with its properties such as superposition, entanglement, and quantum interference, is considered a candidate computing paradigm that may provide acceleration for specific sub-problems and bring new inductive biases to representation learning. This paper focuses on the application potential of quantum computing in artificial intelligence. It first establishes the theoretical foundation of quantum computing, then systematically outlines collaborative pathways that quantum computing can explore from three perspectives: AI training acceleration, data processing and representation learning, and neural networks and combinatorial optimization. Furthermore, this paper focuses on the most feasible quantum machine learning (QML) architectures for the Noisy Intermediate-Scale Quantum (NISQ) era, deeply comparing the design philosophy, data encoding strategies, parameter shift rule, shot complexity, and barren plateaus of Variational Quantum Circuits (VQC) and Quantum Neural Networks (QNN). Finally, this paper analyzes potential application areas in finance, pharmaceuticals/materials, natural language processing, and recommendation systems, and discusses key obstacles such as input/output bottlenecks, noise and error mitigation, benchmarking and verifiability, and governance and security. The paper concludes that in the short to medium term, quantum computing is more likely to serve as a "co-processor" for classical AI, providing gains in specific subtasks through a hybrid quantum-classical workflow. In the long term, however, the development of fault-tolerant quantum computing and scalable quantum error correction is necessary to achieve reproducible and cost-effective quantum advantages across a wider range of AI tasks.
The rapid development of deep learning and large language models (LMs) has led to a "computing inflation" phenomenon in the demand for computing power, energy consumption, and training time for artificial intelligence (AI). As classical computing gradually approaches its scalability bottleneck due to the slowing of Moore's Law and limitations imposed by memory walls and energy consumption, quantum computing (QC), with its properties such as superposition, entanglement, and quantum interference, is considered a candidate computing paradigm that may provide acceleration for specific sub-problems and bring new inductive biases to representation learning. This paper focuses on the application potential of quantum computing in artificial intelligence. It first establishes the theoretical foundation of quantum computing, then systematically outlines collaborative pathways that quantum computing can explore from three perspectives: AI training acceleration, data processing and representation learning, and neural networks and combinatorial optimization. Furthermore, this paper focuses on the most feasible quantum machine learning (QML) architectures for the Noisy Intermediate-Scale Quantum (NISQ) era, deeply comparing the design philosophy, data encoding strategies, parameter shift rule, shot complexity, and barren plateaus of Variational Quantum Circuits (VQC) and Quantum Neural Networks (QNN). Finally, this paper analyzes potential application areas in finance, pharmaceuticals/materials, natural language processing, and recommendation systems, and discusses key obstacles such as input/output bottlenecks, noise and error mitigation, benchmarking and verifiability, and governance and security. The paper concludes that in the short to medium term, quantum computing is more likely to serve as a "co-processor" for classical AI, providing gains in specific subtasks through a hybrid quantum-classical workflow. In the long term, however, the development of fault-tolerant quantum computing and scalable quantum error correction is necessary to achieve reproducible and cost-effective quantum advantages across a wider range of AI tasks.
The field of Quantum Artificial Intelligence (Quantum AI) has emerged as a promising interdisciplinary research area where the computational power of quantum computing is merged with the learning and decision-making power of AI. The application of quantum principles such as superposition, entanglement and quantum interference to machine learning algorithms has created new possibilities for solving problems that may be difficult to solve by classical machines. In the last few years, advances in Noisy Intermediate-Scale Quantum (NISQ) devices have helped to speed advances in Quantum Machine Learning (QML) algorithms, Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Convolutional Neural Networks (QCNNs) and hybrid learning algorithms. This review provides a comprehensive overview of the research in the field of Quantum AI from 2020 to 2026, covering the key concepts, novel architectures, algorithmic advancements, and practical applications. A critical assessment of the application of Quantum AI in fields such as healthcare, finance, cybersecurity, scientific computing and optimization problems is provided, and the benefits of quantum-enhanced learning models are discussed. In addition, common difficulties such as quantum hardware limitations, sensitivity to noise, encoding of data, scalability constraints and a lack of speed in training variational quantum circuits are discussed. The review also covers new avenues of research, including trustworthy quantum machine learning, explainable Quantum AI, quantum federated learning, and fault-tolerant quantum computing. The results suggest while practical large-scale deployment is currently limited by existing hardware capabilities, hybrid quantum-classical systems have great potential to rapidly move intelligent data processing and develop next-generation AI systems.
Quantum Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks
This paper explores the transformative intersection of quantum computing and artificial intelligence, a synergy poised to redefine the limits of computational power. As classical deep learning approaches the "scaling wall," quantum-enhanced architectures offer a path forward through superior optimization and high-dimensional data processing. Abstract / Research Description The Quantum AI Convergence provides a comprehensive technical audit of how quantum mechanics is being integrated into modern machine learning workflows. The research transitions from theoretical foundations to practical enterprise deployment, focusing on three core pillars: 1. Hybrid Quantum-Classical Architectures The study analyzes the current state of Noisy Intermediate-Scale Quantum (NISQ) devices. It explores the efficacy of Quantum Neural Networks (QNNs) and Variational Quantum Eigensolvers (VQE), demonstrating how hybrid models utilize classical hardware for backpropagation while leveraging quantum circuits for complex feature mapping. 2. Enterprise Scaling and Application Beyond the laboratory, the paper investigates high-impact industry use cases where quantum advantage is becoming tangible: Pharmaceuticals: Accelerating molecular docking and drug discovery through quantum-enhanced simulations. Finance: Optimizing multi-variable portfolios and risk assessment models that exceed the capacity of Monte Carlo simulations. Logistics: Solving NP-hard optimization problems in global supply chain routing. 3. The Post-Quantum Paradigm As AI models become more powerful, the research addresses the critical security "double-edged sword." It evaluates the necessity of Post-Quantum Cryptography (PQC) to protect sensitive AI training data against future Shor’s algorithm-based attacks, ensuring that the evolution of intelligence does not come at the cost of absolute privacy. Key Technical Themes Quantum Supremacy vs. Quantum Advantage: Distinguishing between theoretical milestones and practical business value. Tensor Networks: Their role as a bridge between classical deep learning and quantum state representations. Algorithmic Efficiency: Comparing the complexity classes of classical vs. quantum kernels, specifically focusing on Grover’s and HHL algorithms. Research Objective: To provide a roadmap for CTOs and researchers to transition from "Quantum Curious" to "Quantum Ready," establishing a framework for integrating quantum-enhanced AI into existing enterprise tech stacks.
As the limits of classical machine learning (ML) become increasingly evident—especially in terms of computational scalability and data processing—quantum computing emerges as a promising frontier capable of addressing these challenges. Quantum Machine Learning (QML) represents the intersection of quantum computing and traditional ML, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance learning capabilities, accelerate training, and solve complex problems that are intractable for classical algorithms. This paper provides a comprehensive overview of the evolving field of QML, beginning with the theoretical foundations of quantum computing and its synergy with classical machine learning principles. We explore key QML algorithms, including quantum support vector machines (QSVM), variational quantum classifiers (VQC), quantum neural networks (QNN), and hybrid quantum-classical approaches that exemplify the practical integration of both paradigms. We further examine real-world applications of QML across domains such as drug discovery, finance, climate modeling, and optimization, where quantum enhancements have begun to show early but promising results. Despite its theoretical potential, QML faces substantial limitations, including quantum hardware instability, limited qubit scalability, noise sensitivity, and unresolved challenges in quantum data encoding and interpretability. In synthesizing recent advancements and persistent obstacles, this paper highlights the importance of continued interdisciplinary research, algorithmic refinement, and hardware evolution. We also outline future directions that could lead QML from experimental promise to practical utility, including developments in quantum error correction, benchmarking standards, and application-specific quantum architectures
Abstract Quantum computing when combined with artificial intelligence (AI) offers a novel model of computation, which can overcome the fundamental drawbacks of classical systems in terms of complexity, scale, and energy consumption. Empirical evidence is scarce in the literature on quantum machine learning, as most studies mainly focus on theoretical advantages that are limited by hardware noise, short qubit coherence times, and NISQ-era constraints. This research is intended to compare quantum models of AI with hybrid quantum-classical AI models in terms of computational performance, performance improvement, and energy consumption. The article considers quantum neural networks and hybrid learning systems by applying quantum concepts to machine learning and optimization problems in healthcare, financial systems, IoT security, and scientific computing. Experiments indicate 35–47% improvement in pattern recognition accuracy, 62% reduction in training time, and 78% improvement in energy consumption, along with a reduction in computational complexity from O(N²) to O(N log N). The article provides relevant evidence of quantum benefits in accordance with NISQ criteria and presents a comparison between classical and quantum AI systems using benchmarks. The study confirms the possible use of quantum-driven AI in current limited scenarios, while fault-tolerant architectures and scalable quantum systems emerge as significant prospects for future research. Keywords Quantum Machine Learning, Quantum Neural Networks, Hybrid Quantum-Classical Computing, Quantum Optimization, NISQ Era, Quantum Advantage, Quantum Entanglement, Superposition, Quantum Gates, Variational Quantum Algorithms