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 networks and systems

Evaluating the Performance of Hybrid Quantum-Classical Convolutional Neural Networks on NISQ Devices

Tristan Nagan, Ritesh Ajoodha

Published Oct 26, 2021• 0 citations
Publisher
DOI
10.1007/978-981-16-2102-4_20

Related papers

Hybrid Quantum–Classical Convolutional Neural Networks for Medical Image Classification: A Review of Architectures, Applications, and NISQ Challenges

Fatima Kamran, M. Turki-Hadj Alouane

Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Quantum–Classical Convolutional Neural Networks (HQCNNs) have been developed, using quantum properties like superposition and entanglement to improve feature extraction and classification of medical images. This review offers a thorough analysis of 50 peer-reviewed studies published from 2021 to 2026 and introduces a taxonomy of HQCNN architectures. It classifies existing approaches into three categories: Quantum-to-Classical, Classical-to-Quantum, and Hybrid Quantum Transfer Learning. The studies reviewed encompass a diverse range of medical imaging applications, including binary and multi-class disease classification using X-ray, CT, MRI, ultrasound, dermoscopic, retinal, and mammographic images. Additionally, the review compares HQCNNs with their classical CNN counterparts, exploring how factors like quantum circuit design, dataset size, class imbalance, task complexity, evaluation protocols, and hardware limitations influence classification performance. It concludes with an analysis of the key limitations of current HQCNN models, discusses challenges in deploying NISQ devices practically, and proposes future research directions to bridge the gap between experimental HQCNN frameworks and reliable clinical diagnostic systems.

Electronics•Oct 11, 2026•0 citations

Quantum enhanced convolutional neural networks for NISQ computers

Dong-Ling Deng

Science China Physics Mechanics and Astronomy•Aug 24, 2021•8 citations

Hybrid quantum-classical convolutional neural networks

Junhua Liu, Kwan Hui Lim, Kristin Lee Wood +3 more

arXiv:1911.02998•Science China Physics Mechanics and Astronomy•Aug 4, 2021•291 citations

Hybrid Quantum-Classical Neural Networks: Bridging Deep Learning and Quantum Computing for Image Classification

Akash Jha, Rahul Bhandari, Raman Chadha

Given the recent prominence of Noisy Intermediate Scale Quantum (NISQ) devices, hybrid quantum-classical neural networks have recently drawn a lot of excitement as a potential solution to improving learning efficiency. Our current paper describes a Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) which is defined as a pretrained backbone of ResNet-18 and a small quantum-enhanced feature-transformation layer of Qiskit. The convolutional high-dimensional features are reduced to the classical eight-dimensional one, which in turn is encoded into a four-qubit parameterized quantum circuit. The resultant quantum expectation values are combined with the compressed features to come up with a hybridized representation of twelve dimensions, used to classify multi-class dataset. Experimental analysis of CIFAR10 data has shown that the suggested HQCNN achieves a test accuracy of 94.74% and improves class-wise precision, recall, and F1-scores than a number of existing hybrid models while using a significantly smaller number of latent features. More investigation into the quantum module indicates that it is predictable about scalability to batch, resistant to perturbations in features, and efficiently performed by evaluating circuit vectors. These results indicate that compact and expressive representations of images that can be helpful in the NISQ era can be acquired using hybrid architectures, thus providing a practical way to forward the benefits of quantum to modern deep-learning pipelines.

International Conference on Emerging Computational Intelligence (ICECI)•Apr 4, 2026•0 citations

Quantum Neural Networks in the NISQ Era: Architectures, Challenges, and Applications

Kanishka W. Palihakkara, Mahesh N. Jayakody

Proceedings in Technology Transfer•Jan 1, 2026•0 citations

A comparative analysis and noise robustness evaluation in quantum neural networks

Tasnim Ahmed, Muhammad Kashif, Alberto Marchisio +1 more

In current noisy intermediate-scale quantum (NISQ) devices, hybrid quantum neural networks (HQNNs) offer a promising solution, combining the strengths of classical machine learning with quantum computing capabilities. However, the performance of these networks can be significantly affected by the quantum noise inherent in NISQ devices. In this paper, we conduct an extensive comparative analysis of various HQNN algorithms, namely Quantum Convolution Neural Network (QCNN), Quanvolutional Neural Network (QuanNN), and Quantum Transfer Learning (QTL), for image classification tasks. We evaluate the performance of each algorithm across quantum circuits with different entangling structures, variations in layer count, and optimal placement in the architecture. Subsequently, we select the highest-performing architectures and assess their robustness against noise influence by introducing quantum gate noise through Phase Flip, Bit Flip, Phase Damping, Amplitude Damping, and the Depolarization Channel. Our results reveal that the top-performing models exhibit varying resilience to different noise channels. However, in most scenarios, the QuanNN demonstrates greater robustness across various quantum noise channels, consistently outperforming other models. This highlights the importance of tailoring model selection to specific noise environments in NISQ devices.

Scientific Reports•Sep 29, 2025•22 citations
Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Quantum–Classical Convolutional Neural Networks (HQCNNs) have been developed, using quantum properties like superposition and entanglement to improve feature extraction and classification of medical images. This review offers a thorough analysis of 50 peer-reviewed studies published from 2021 to 2026 and introduces a taxonomy of HQCNN architectures. It classifies existing approaches into three categories: Quantum-to-Classical, Classical-to-Quantum, and Hybrid Quantum Transfer Learning. The studies reviewed encompass a diverse range of medical imaging applications, including binary and multi-class disease classification using X-ray, CT, MRI, ultrasound, dermoscopic, retinal, and mammographic images. Additionally, the review compares HQCNNs with their classical CNN counterparts, exploring how factors like quantum circuit design, dataset size, class imbalance, task complexity, evaluation protocols, and hardware limitations influence classification performance. It concludes with an analysis of the key limitations of current HQCNN models, discusses challenges in deploying NISQ devices practically, and proposes future research directions to bridge the gap between experimental HQCNN frameworks and reliable clinical diagnostic systems.
Given the recent prominence of Noisy Intermediate Scale Quantum (NISQ) devices, hybrid quantum-classical neural networks have recently drawn a lot of excitement as a potential solution to improving learning efficiency. Our current paper describes a Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) which is defined as a pretrained backbone of ResNet-18 and a small quantum-enhanced feature-transformation layer of Qiskit. The convolutional high-dimensional features are reduced to the classical eight-dimensional one, which in turn is encoded into a four-qubit parameterized quantum circuit. The resultant quantum expectation values are combined with the compressed features to come up with a hybridized representation of twelve dimensions, used to classify multi-class dataset. Experimental analysis of CIFAR10 data has shown that the suggested HQCNN achieves a test accuracy of 94.74% and improves class-wise precision, recall, and F1-scores than a number of existing hybrid models while using a significantly smaller number of latent features. More investigation into the quantum module indicates that it is predictable about scalability to batch, resistant to perturbations in features, and efficiently performed by evaluating circuit vectors. These results indicate that compact and expressive representations of images that can be helpful in the NISQ era can be acquired using hybrid architectures, thus providing a practical way to forward the benefits of quantum to modern deep-learning pipelines.
In current noisy intermediate-scale quantum (NISQ) devices, hybrid quantum neural networks (HQNNs) offer a promising solution, combining the strengths of classical machine learning with quantum computing capabilities. However, the performance of these networks can be significantly affected by the quantum noise inherent in NISQ devices. In this paper, we conduct an extensive comparative analysis of various HQNN algorithms, namely Quantum Convolution Neural Network (QCNN), Quanvolutional Neural Network (QuanNN), and Quantum Transfer Learning (QTL), for image classification tasks. We evaluate the performance of each algorithm across quantum circuits with different entangling structures, variations in layer count, and optimal placement in the architecture. Subsequently, we select the highest-performing architectures and assess their robustness against noise influence by introducing quantum gate noise through Phase Flip, Bit Flip, Phase Damping, Amplitude Damping, and the Depolarization Channel. Our results reveal that the top-performing models exhibit varying resilience to different noise channels. However, in most scenarios, the QuanNN demonstrates greater robustness across various quantum noise channels, consistently outperforming other models. This highlights the importance of tailoring model selection to specific noise environments in NISQ devices.