Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2503.01916 · IEEE Transactions on Intelligent Transportation Systems

QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

Ashtakala Meghanath, Subham Das, Bikash K. Behera, Muhammad Attique Khan, Saif M. Al-Kuwari, Ahmed Farouk

Published Mar 18, 2025• 13 citations
arXivHTMLPDF

Abstract

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU$\dagger $method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate QDCNN’s —key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework.

DOI
10.1109/tits.2025.3546353

Related papers

Dynamic Multi-Pqc Quantum Convolutional Neural Network for Real-Time Pothole Detection: Invited Paper

Minjoo Kim, Juhui Heo, Emily Jimin Roh +1 more

This paper presents a novel quantum artificial intelligence model for efficient and accurate pothole detection on road surfaces, specifically tailored for autonomous vehicle (AV) environments. Potholes are widely recognized in the literature as critical risk factors that can cause significant vehicle damage and contribute to traffic accidents. Accordingly, timely and precise detection of potholes is essential to ensure the safety of both vehicles and their occupants. To meet the stringent real-time constraints of AV navigation, the proposed approach employs quantum convolutional neural networks (QCNNs), which provide enhanced expressiveness, low-latency inference, and fewer parameters while achieving performance comparable to classical convolutional neural networks (CNNs). To ensure algorithmic stability under various road and traffic dynamics, a Lyapunov optimization strategy is integrated into the QCNN architecture. This enables the design of a stabilized learning framework, which dynamically regulates the detection model to maintain robustness and performance even in fluctuating environments. Experiments show that the proposed QCNN outperforms classical CNNs in both accuracy and efficiency. In addition, the proposed Lyapunov optimization-based QCNN architecture control further enhances real-time performance, considering inference delay.

IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)•Jul 7, 2025•1 citation

QCACNN: A Quantum Convolutional Neural Network Algorithm for Traffic Sign Recognition in Carbon-Intelligent Electric Vehicles

Zhiguo Qu, Yichen Xia, Le Sun +2 more

Traffic sign recognition is essential for autonomous driving, enhancing driving efficiency and ensuring road safety. Traffic signs utilize colors and patterns to relay critical information, with color accuracy being especially significant. Traditional models, reliant on extensive data and computing resources, struggle to meet the efficiency demands of electric vehicles with carbon-intelligent computing. Leveraging the physical properties of quantum superposition states, quantum computing offers a solution with its unique parallel computing capabilities, potentially enhancing recognition efficiency and facilitating real-time processing. Quantum convolutional neural networks (QCNNs) show promise in processing large-scale image data with improved efficiency and accuracy. However, most of QCNN researches focus on grayscale image classification, with limited studies on multichannel data. This article introduces the quantum channel attention convolutional neural network (QCACNN), which incorporates a quantum channel attention layer (QCAL) to enhance multichannel data classification. The experimental results demonstrate that QCACNNs surpasses traditional QCNNs in traffic sign recognition, performing comparably to conventional CNNs and SENet models with fewer computing resources. Detailed performance analysis and ablation studies validate the effectiveness of each component within this architecture. Quantum noise resistance tests confirm the robustness of QCACNN, making it a viable solution for electric vehicles with enhanced scalability.

IEEE Internet of Things Journal•Mar 24, 2025•10 citations

QNN-VRCS: A Quantum Neural Network for Vehicle Road Cooperation Systems

Nouhaila Innan, Bikash K. Behera, Saif M. Al-Kuwari +1 more

The escalating complexity of urban transportation systems, increased by traffic congestion, diverse transportation modalities, and shifting commuter preferences, necessitates developing more sophisticated analytical frameworks. Traditional computational approaches often struggle with the voluminous datasets generated by real-time sensor networks, and they generally lack the precision needed for accurate traffic prediction and efficient system optimization. Therefore, we integrate quantum computing techniques to enhance Vehicle Road Cooperation Systems (VRCS). By leveraging quantum algorithms, specifically$UU^{\dagger }$and variational$UU^{\dagger }$, in conjunction with quantum image encoding methods such as Flexible Representation of Quantum Images (FRQI) and Novel Enhanced Quantum Representation (NEQR), we propose an optimized Quantum Neural Network (QNN). The QNN features adjustments in its entangled layer structure and training duration to handle traffic data processing complexities better. Empirical evaluations on two traffic datasets show that our model achieves superior classification accuracies of 97.42% and 84.08% and demonstrates remarkable robustness in various noise conditions. Our study underscores the potential of quantum-enhanced 6G solutions in streamlining complex transportation systems, highlighting the pivotal role of quantum technologies in advancing intelligent transportation solutions.

arXiv:2412.12705•IEEE Transactions on Intelligent Transportation Systems•Feb 14, 2025•21 citations

QDCNN: Quantum Dilated Convolutional Neural Network

Yixiong Chen

In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements, has been massively explored for the purpose of improving the performance of classical neural networks. In this paper, we propose a novel hybrid quantum-classical algorithm called quantum dilated convolutional neural networks (QDCNNs). Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks. The proposed QDCNNs are able to capture larger context during the quantum convolution process while reducing the computational cost. We perform empirical experiments on MNIST and Fashion-MNIST datasets for the task of image recognition and demonstrate that QDCNN models generally enjoy better performances in terms of both accuracy and computation efficiency compared to existing quantum convolutional neural networks (QCNNs).

arXiv:2110.15667•Oct 29, 2021•6 citations

Computation-efficient quantum convolutional neural networks for autonomous driving applications

Emily Jimin Roh, Chaemoon Im, Wonjun Jeong +1 more

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.

The Journal of Supercomputing•Jun 14, 2025•5 citations

Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications

Emily Jimin Roh, Hankyul Baek, Donghyeon Kim +1 more

Object detection applications, especially in autonomous driving, have drawn attention due to the advancements in deep learning. Additionally, with continuous improvements in classical convolutional neural networks (CNNs), there has been a notable enhancement in both the efficiency and speed of these applications, making autonomous driving more reliable and effective. However, due to the exponentially rapid growth in the complexity and scale of visual signals used in object detection, there are limitations regarding computation speeds while conducting object detection solely with classical computing. Motivated by this, this paper proposes the quantum object detection engine (QODE), which implements a quantum version of CNN, named QCNN, in object detection. Furthermore, this paper proposes a novel fast quantum convolution algorithm that processes the multi-channel of visual signals based on a small number of qubits and constructs the output channel data, thereby achieving relieved computational complexity. Our QODE, equipped with fast quantum convolution, demonstrates feasibility in object detection with multi-channel data, addressing a limitation of current QCNNs due to the scarcity of qubits in the current era of quantum computing. Moreover, this paper introduces a heterogeneous knowledge distillation training algorithm that enhances the performance of our QODE.

IEEE Transactions on Mobile Computing•Sep 27, 2024•35 citations
In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU†\dagger †method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate QDCNN’s —key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework.
This paper presents a novel quantum artificial intelligence model for efficient and accurate pothole detection on road surfaces, specifically tailored for autonomous vehicle (AV) environments. Potholes are widely recognized in the literature as critical risk factors that can cause significant vehicle damage and contribute to traffic accidents. Accordingly, timely and precise detection of potholes is essential to ensure the safety of both vehicles and their occupants. To meet the stringent real-time constraints of AV navigation, the proposed approach employs quantum convolutional neural networks (QCNNs), which provide enhanced expressiveness, low-latency inference, and fewer parameters while achieving performance comparable to classical convolutional neural networks (CNNs). To ensure algorithmic stability under various road and traffic dynamics, a Lyapunov optimization strategy is integrated into the QCNN architecture. This enables the design of a stabilized learning framework, which dynamically regulates the detection model to maintain robustness and performance even in fluctuating environments. Experiments show that the proposed QCNN outperforms classical CNNs in both accuracy and efficiency. In addition, the proposed Lyapunov optimization-based QCNN architecture control further enhances real-time performance, considering inference delay.
Traffic sign recognition is essential for autonomous driving, enhancing driving efficiency and ensuring road safety. Traffic signs utilize colors and patterns to relay critical information, with color accuracy being especially significant. Traditional models, reliant on extensive data and computing resources, struggle to meet the efficiency demands of electric vehicles with carbon-intelligent computing. Leveraging the physical properties of quantum superposition states, quantum computing offers a solution with its unique parallel computing capabilities, potentially enhancing recognition efficiency and facilitating real-time processing. Quantum convolutional neural networks (QCNNs) show promise in processing large-scale image data with improved efficiency and accuracy. However, most of QCNN researches focus on grayscale image classification, with limited studies on multichannel data. This article introduces the quantum channel attention convolutional neural network (QCACNN), which incorporates a quantum channel attention layer (QCAL) to enhance multichannel data classification. The experimental results demonstrate that QCACNNs surpasses traditional QCNNs in traffic sign recognition, performing comparably to conventional CNNs and SENet models with fewer computing resources. Detailed performance analysis and ablation studies validate the effectiveness of each component within this architecture. Quantum noise resistance tests confirm the robustness of QCACNN, making it a viable solution for electric vehicles with enhanced scalability.
The escalating complexity of urban transportation systems, increased by traffic congestion, diverse transportation modalities, and shifting commuter preferences, necessitates developing more sophisticated analytical frameworks. Traditional computational approaches often struggle with the voluminous datasets generated by real-time sensor networks, and they generally lack the precision needed for accurate traffic prediction and efficient system optimization. Therefore, we integrate quantum computing techniques to enhance Vehicle Road Cooperation Systems (VRCS). By leveraging quantum algorithms, specificallyUU†UU^{\dagger }UU†and variationalUU†UU^{\dagger }UU†, in conjunction with quantum image encoding methods such as Flexible Representation of Quantum Images (FRQI) and Novel Enhanced Quantum Representation (NEQR), we propose an optimized Quantum Neural Network (QNN). The QNN features adjustments in its entangled layer structure and training duration to handle traffic data processing complexities better. Empirical evaluations on two traffic datasets show that our model achieves superior classification accuracies of 97.42% and 84.08% and demonstrates remarkable robustness in various noise conditions. Our study underscores the potential of quantum-enhanced 6G solutions in streamlining complex transportation systems, highlighting the pivotal role of quantum technologies in advancing intelligent transportation solutions.
In recent years, with rapid progress in the development of quantum technologies, quantum machine learning has attracted a lot of interest. In particular, a family of hybrid quantum-classical neural networks, consisting of classical and quantum elements, has been massively explored for the purpose of improving the performance of classical neural networks. In this paper, we propose a novel hybrid quantum-classical algorithm called quantum dilated convolutional neural networks (QDCNNs). Our method extends the concept of dilated convolution, which has been widely applied in modern deep learning algorithms, to the context of hybrid neural networks. The proposed QDCNNs are able to capture larger context during the quantum convolution process while reducing the computational cost. We perform empirical experiments on MNIST and Fashion-MNIST datasets for the task of image recognition and demonstrate that QDCNN models generally enjoy better performances in terms of both accuracy and computation efficiency compared to existing quantum convolutional neural networks (QCNNs).
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.
Object detection applications, especially in autonomous driving, have drawn attention due to the advancements in deep learning. Additionally, with continuous improvements in classical convolutional neural networks (CNNs), there has been a notable enhancement in both the efficiency and speed of these applications, making autonomous driving more reliable and effective. However, due to the exponentially rapid growth in the complexity and scale of visual signals used in object detection, there are limitations regarding computation speeds while conducting object detection solely with classical computing. Motivated by this, this paper proposes the quantum object detection engine (QODE), which implements a quantum version of CNN, named QCNN, in object detection. Furthermore, this paper proposes a novel fast quantum convolution algorithm that processes the multi-channel of visual signals based on a small number of qubits and constructs the output channel data, thereby achieving relieved computational complexity. Our QODE, equipped with fast quantum convolution, demonstrates feasibility in object detection with multi-channel data, addressing a limitation of current QCNNs due to the scarcity of qubits in the current era of quantum computing. Moreover, this paper introduces a heterogeneous knowledge distillation training algorithm that enhances the performance of our QODE.