Autonomous driving systems demand real-time processing of high-dimensional sensor data, large-scale decision making, NP-hard routing optimization under strict latency and computational constraints. Classical approaches using convolutional neural networks face scalability issues and performance limitations in dynamic environments. IBM’s Qiskit framework provides a comprehensive solution with a modular architecture, enabling the construction of quantum circuits that encode multi-sensor data into exponentially large Hilbert spaces. The core components like Terra and Aer support circuit design and noise-aware simulation. Additional libraries extend these capabilities to support quantum machine learning and optimization algorithms. This paper presents a unified Qiskit-based framework incorporating quantum neural networks for trajectory prediction, quantum convolutional neural networks for safety classification in poor conditions, and quantum approximate optimization algorithm for efficient vehicle routing. We showcase how quantum superposition explores multiple routing paths in parallel and how quantum circuits represent grid locations to improve path finding. Our survey reveals measurable performance advantages of quantum-enhanced models over classical methods, forming the basis of quantum-accelerated autonomous driving systems.
Autonomous driving systems demand real-time processing of high-dimensional sensor data, large-scale decision making, NP-hard routing optimization under strict latency and computational constraints. Classical approaches using convolutional neural networks face scalability issues and performance limitations in dynamic environments. IBM’s Qiskit framework provides a comprehensive solution with a modular architecture, enabling the construction of quantum circuits that encode multi-sensor data into exponentially large Hilbert spaces. The core components like Terra and Aer support circuit design and noise-aware simulation. Additional libraries extend these capabilities to support quantum machine learning and optimization algorithms. This paper presents a unified Qiskit-based framework incorporating quantum neural networks for trajectory prediction, quantum convolutional neural networks for safety classification in poor conditions, and quantum approximate optimization algorithm for efficient vehicle routing. We showcase how quantum superposition explores multiple routing paths in parallel and how quantum circuits represent grid locations to improve path finding. Our survey reveals measurable performance advantages of quantum-enhanced models over classical methods, forming the basis of quantum-accelerated autonomous driving systems.
This paper proposes a computation-efficient quantum convolutional neural network (CE-QCNN) architecture designed for autonomous driving applications. A key contribution of this work lies in the formulation of a tri-value qubit encoding (TQE) scheme, which compactly embeds three-channel RGB image data into single-qubit states via a sequence of quantum rotations. This strategy enables significant qubit resource reduction while preserving the representational richness of multi-channel visual inputs. The encoded quantum states are subsequently processed through parameterized quantum circuits for convolutional feature extraction, forming the core of the proposed CE-QCNN framework. To further improve learning stability and early-stage performance, a knowledge distillation (KD) strategy is employed, transferring supervision from a pretrained classical CNN model to the quantum network. The proposed model is evaluated on the KITTI dataset, a standard benchmark for autonomous driving, where it demonstrates both competitive detection accuracy and reduced computational complexity. These results substantiate the scalability and practical applicability of CE-QCNNs for future quantum-enhanced perception systems in real-time autonomous driving scenarios.
Real-time processing with high classification accuracy is a fundamental requirement in autonomous driving systems. However, existing neural network models for classification often face a tradeoff between computational efficiency and accuracy, necessitating the development of advanced optimization methods to address this limitation. Additionally, dynamic driving environments offer opportunities to enhance classification performance by leveraging the principles of quantum computing, particularly the properties of superposition and entanglement. In response to these challenges, a multi-stage quantum convolutional neural network (MS-QCNN) approach is proposed, designed to improve image analysis performance by effectively utilizing the multi-stage structure of QCNN. A Lyapunov optimization framework is applied to achieve optimal performance, which maximizes time-averaged efficiency while ensuring system stability. This framework dynamically adjusts the MS-QCNN model in response to environmental variations, promoting enhanced queue stability and achieving optimal time-averaged performance.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
In this paper, we present an approach to enhance the intelligence of autonomous vehicles by focusing on real-time collaborative route planning. This aspect is critical not only for individual vehicles but also for ensuring effective interaction among multiple autonomous systems. The challenge is to optimize route planning algorithms for real-time operation without compromising accuracy. To address this, we leverage quantum computing techniques, integrating them with CNN architectures to develop Quantum Convolutional Neural Networks (QCNNs) and the Optimized QCNN. Neural networks play a key role in tasks such as predicting paths, estimating traffic flow, and selecting optimal routes, ensuring efficient and adaptive planning. The integration of quantum computing with neural networks significantly enhances real-time performance and the Optimized QCNN, developed using the Circuit Depth Reduction strategy, further improves accuracy and convergence rate. Our evaluation, conducted on the Cityscapes Dataset, demonstrates that QCNNs comparing with traditional CNNs achieve faster convergence rate and lower loss value. Furthermore, Optimized QCNNs surpass standard QCNNs in both convergence rate and loss, while also achieving higher accuracy even than traditional CNNs, making them a powerful model for real-time collaborative route planning in autonomous vehicles.
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.
The rapid convergence of quantum computing and artificial intelligence (AI) marks a transformative phase in computational sciences, promising groundbreaking solutions to problems beyond the reach of classical systems. This paper provides a comprehensive survey of the theoretical foundations, key advancements, and future challenges of integrating quantum computing into AI. The discussion begins with foundational quantum concepts, including the principles of superposition, entanglement, and interference, as well as mathematical frameworks such as Dirac notation and tensor product spaces. These concepts form the backbone of quantum algorithms and architectures designed to enhance AI capabilities. The paper delves into the state-of-the-art in quantum hardware, exploring the construction of quantum gates, circuits, and the innovations driving superconducting qubits, trapped ions, and photonic quantum systems. It reviews the contributions of leading industry and academic players in overcoming scalability and error correction challenges. Building on these hardware advancements, the survey examines prominent quantum algorithms, such as Shor’s algorithm for cryptography, Grover’s algorithm for search and optimization, and their potential to accelerate machine learning tasks. A significant portion of the survey focuses on quantum machine learning (QML), covering core architectures like Quantum Neural Networks (QNNs), Variational Quantum Circuits (VQCs), and Quantum Boltzmann Machines (QBMs). The paper also explores Quantum Support Vector Machines (QSVM) and Quantum Principal Component Analysis (QPCA), emphasizing their implementation, data encoding strategies, and optimization processes. Despite these advances, the paper identifies persistent challenges that hinder widespread adoption, including quantum decoherence, hardware scalability, and the need for modular architectures. Additionally, it considers the ethical and economic implications of quantum-AI integration, particularly as quantum technologies evolve from theoretical models to practical applications. The insights presented here aim to guide researchers, practitioners, and policymakers in shaping the future of this transformative field.