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

Lecture notes in computer science

Single-Qubit Multi-data Encoding for Efficient Quantum Convolution in Image Recognition

Lianghai Chen, Xiaoliang Wang, Yue Liu, S. Deng, Yuzhen Liu, Huaning Song

Published Jul 14, 2026• 0 citations
Publisher
DOI
10.1007/978-981-92-2868-3_23

Related papers

A qubit-efficient quantum algorithm for convolutional feature extraction

Mohammad Rasoul Roshanshah, Payman Kazemikhah, Hossein Aghababa +1 more

Quantum Machine Intelligence•Jul 30, 2026•0 citations

QMDUC: quantum multi-channel data uploading convolution for image classification

Han Qi, Shengjun Wang, Hao Wang +2 more

Abstract Hybrid Quantum-Classical Convolutional Neural Networks (HQCNNs) show significant potential for image classification by embedding quantum circuits as convolutional kernels within classical CNNs, leveraging quantum computing to enhance feature extraction. While this approach provides a feasible pathway toward achieving quantum advantage under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints, existing HQCNNs face critical challenges, including limited classification accuracy and excessive quantum resource consumption when processing multi-channel data. To address these issues, this paper proposes the Quantum Multi-channel Data Uploading Convolution (QMDUC) method, which employs data re-uploading techniques to optimize quantum data encoding strategies, significantly reducing qubit requirements while maintaining complex data processing capabilities. QMDUC supports two encoding modes: Channel-Priority QMDUC (CP-QMDUC) and Data-Priority QMDUC (DP-QMDUC), enabling flexible trade-offs between qubit count and circuit depth, thereby providing adaptive solutions tailored to different quantum hardware constraints. Experimental validation on the CIFAR-10 three-channel color image dataset and Fashion-MNIST single-channel grayscale image dataset demonstrates that QMDUC significantly outperforms existing HQCNN methods in both classification accuracy and quantum resource efficiency. Specifically, under low-resource configurations, the proposed method achieves an approximate reduction in qubit requirements of 75%–95% while maintaining comparable or superior accuracy; under higher-resource configurations, it achieves approximately 10% improvement in peak accuracy with comparable qubit consumption to existing methods, providing an effective technical pathway for practical applications of quantum machine learning in computer vision.

Physica Scripta•Mar 20, 2026•0 citations

Hybrid Quantum Image Recognition on Limited-Qubit Hardware

Olaf Duda, Mateusz Andrzej Kucharski, Radosław Marcin Idzikowski

Lecture notes in networks and systems•Jan 1, 2026•0 citations

Enhancing image classification with multi-data-per-qubit processing: A high-efficiency quantum convolutional scheme

Xiaoliang Wang, Lianghai Chen, Yi Lu +3 more

As an emerging research direction in quantum computing, quantum convolutional neural networks provide a promising way to combine quantum feature processing with traditional machine learning algorithms; however, their development is constrained by the scarcity of quantum resources. In this work, we propose a quantum-classical hybrid convolutional neural network model based on quantum convolution kernels. This model employs a multi-data-per-qubit rotation-angle encoding scheme to embed multiple data points into a small number of qubits, aiming to reduce quantum resource consumption and enhance feature extraction for image classification tasks. Its compact structure is designed to extract multidimensional features with fewer qubits and reduced measurement overhead, thereby improving the utilization of limited quantum resources under the considered setting. To evaluate our method, we conducted several experiments to compare our model with existing approaches and to assess performance under varying numbers of qubits, different CNOT gate configurations, and diverse encoding schemes. Experimental results show that the proposed model achieves competitive performance and slightly higher average accuracy than the traditional convolution structure and three existing quantum convolution schemes under a Qiskit Aer noisy simulation environment. This investigation provides a compact design for the structural optimization and low-resource implementation of quantum convolutional neural networks, offering a potential scheme for quantum-assisted image feature extraction.

Computer Science and Information Systems•Jan 1, 2026•0 citations

Hilbert-Space Quantum Neural Representation Learning for NISQ-Era Quantum Computing

Yijie Zhu

The convergence of quantum computing and machine learning offers significant potential for processing complex data distributions. However, the realization of this potential in the Noisy Intermediate-Scale Quantum (NISQ) era is constrained by physical hardware limitations, particularly restricted qubit counts and system noise. This thesis addresses the challenge of the ”Representation Bottleneck” by establishing a framework for resource-efficient quantum representation learning. The research demonstrates that effective learning on near-term devices can be achieved by optimizing circuit architectures and objective functions to align with hardware constraints. First, to address resource limitations in high-dimensional visual recognition, we propose the Multi-Gate Quantum Convolutional Neural Network (MG-QNN). By employing a Multi-Gate strategy, this architecture optimizes the utilization of quan tum resources within the circuit design. Empirical evaluations on face recognition benchmarks indicate that this approach maintains competitive recognition accuracy while reducing the qubit requirement compared to standard quantum convolutional methods. Second, extending the framework to non-Euclidean data, we introduce the Single-Qubit Graph Neural Network (sQGNN). Utilizing Discrete-Time Quantum Walks, this method encodes graph topologies and relational patterns onto a single qubit. This approach circumvents the need for multi-qubit entangling gates, thereby reducing the computational overhead and noise sensitivity typically associated with processing graph-structured data. Third, in the domain of generative modeling, we present the Single-Qubit Quan tum Convolutional Variational Autoencoder (QCVAE). Addressing the difficulty of generative tasks on constrained devices, this model integrates the Information Bottleneck principle with efficient data re-uploading techniques. The study demonstrates that a single qubit possesses sufficient expressivity to model and reconstruct image distributions effectively when guided by appropriate information theoretic constraints. Finally, to resolve the fundamental limitations of classical loss functions in quantum learning, we establish the Hilbert-Schmidt Information Bottleneck (HSIB). We identify that standard Euclidean metrics often fail to respect the geometric structure of the Hilbert space. The HSIB framework employs the Hilbert Schmidt distance as a tractable geometric measure, providing a learning objective that maximizes information retention while adhering to the intrinsic geometry of quantum states. Collectively, these contributions illustrate that practical quantum machine learning advantage in the NISQ era does not rely solely on scaling hardware, but on the strategic design of resource-efficient architectures and geometrically valid metrics.

•Aug 17, 2026•0 citations

Least significant qubit algorithm for quantum images

Jianzhi Sang, Shen Wang, Qiong Li

Quantum Information Processing•Aug 18, 2016•39 citations
Abstract Hybrid Quantum-Classical Convolutional Neural Networks (HQCNNs) show significant potential for image classification by embedding quantum circuits as convolutional kernels within classical CNNs, leveraging quantum computing to enhance feature extraction. While this approach provides a feasible pathway toward achieving quantum advantage under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints, existing HQCNNs face critical challenges, including limited classification accuracy and excessive quantum resource consumption when processing multi-channel data. To address these issues, this paper proposes the Quantum Multi-channel Data Uploading Convolution (QMDUC) method, which employs data re-uploading techniques to optimize quantum data encoding strategies, significantly reducing qubit requirements while maintaining complex data processing capabilities. QMDUC supports two encoding modes: Channel-Priority QMDUC (CP-QMDUC) and Data-Priority QMDUC (DP-QMDUC), enabling flexible trade-offs between qubit count and circuit depth, thereby providing adaptive solutions tailored to different quantum hardware constraints. Experimental validation on the CIFAR-10 three-channel color image dataset and Fashion-MNIST single-channel grayscale image dataset demonstrates that QMDUC significantly outperforms existing HQCNN methods in both classification accuracy and quantum resource efficiency. Specifically, under low-resource configurations, the proposed method achieves an approximate reduction in qubit requirements of 75%–95% while maintaining comparable or superior accuracy; under higher-resource configurations, it achieves approximately 10% improvement in peak accuracy with comparable qubit consumption to existing methods, providing an effective technical pathway for practical applications of quantum machine learning in computer vision.
As an emerging research direction in quantum computing, quantum convolutional neural networks provide a promising way to combine quantum feature processing with traditional machine learning algorithms; however, their development is constrained by the scarcity of quantum resources. In this work, we propose a quantum-classical hybrid convolutional neural network model based on quantum convolution kernels. This model employs a multi-data-per-qubit rotation-angle encoding scheme to embed multiple data points into a small number of qubits, aiming to reduce quantum resource consumption and enhance feature extraction for image classification tasks. Its compact structure is designed to extract multidimensional features with fewer qubits and reduced measurement overhead, thereby improving the utilization of limited quantum resources under the considered setting. To evaluate our method, we conducted several experiments to compare our model with existing approaches and to assess performance under varying numbers of qubits, different CNOT gate configurations, and diverse encoding schemes. Experimental results show that the proposed model achieves competitive performance and slightly higher average accuracy than the traditional convolution structure and three existing quantum convolution schemes under a Qiskit Aer noisy simulation environment. This investigation provides a compact design for the structural optimization and low-resource implementation of quantum convolutional neural networks, offering a potential scheme for quantum-assisted image feature extraction.
The convergence of quantum computing and machine learning offers significant potential for processing complex data distributions. However, the realization of this potential in the Noisy Intermediate-Scale Quantum (NISQ) era is constrained by physical hardware limitations, particularly restricted qubit counts and system noise. This thesis addresses the challenge of the ”Representation Bottleneck” by establishing a framework for resource-efficient quantum representation learning. The research demonstrates that effective learning on near-term devices can be achieved by optimizing circuit architectures and objective functions to align with hardware constraints. First, to address resource limitations in high-dimensional visual recognition, we propose the Multi-Gate Quantum Convolutional Neural Network (MG-QNN). By employing a Multi-Gate strategy, this architecture optimizes the utilization of quan tum resources within the circuit design. Empirical evaluations on face recognition benchmarks indicate that this approach maintains competitive recognition accuracy while reducing the qubit requirement compared to standard quantum convolutional methods. Second, extending the framework to non-Euclidean data, we introduce the Single-Qubit Graph Neural Network (sQGNN). Utilizing Discrete-Time Quantum Walks, this method encodes graph topologies and relational patterns onto a single qubit. This approach circumvents the need for multi-qubit entangling gates, thereby reducing the computational overhead and noise sensitivity typically associated with processing graph-structured data. Third, in the domain of generative modeling, we present the Single-Qubit Quan tum Convolutional Variational Autoencoder (QCVAE). Addressing the difficulty of generative tasks on constrained devices, this model integrates the Information Bottleneck principle with efficient data re-uploading techniques. The study demonstrates that a single qubit possesses sufficient expressivity to model and reconstruct image distributions effectively when guided by appropriate information theoretic constraints. Finally, to resolve the fundamental limitations of classical loss functions in quantum learning, we establish the Hilbert-Schmidt Information Bottleneck (HSIB). We identify that standard Euclidean metrics often fail to respect the geometric structure of the Hilbert space. The HSIB framework employs the Hilbert Schmidt distance as a tractable geometric measure, providing a learning objective that maximizes information retention while adhering to the intrinsic geometry of quantum states. Collectively, these contributions illustrate that practical quantum machine learning advantage in the NISQ era does not rely solely on scaling hardware, but on the strategic design of resource-efficient architectures and geometrically valid metrics.