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:2509.15722 · IEEE International Conference on Quantum Computing and Engineering (QCE)

Impact of Single Rotations and Entanglement Topologies in Quantum Neural Networks

Marco Mordacci, Michele Amoretti

Published Aug 30, 2025• 0 citations
arXivHTMLPDF

Abstract

In this work, an analysis of the performance of different Variational Quantum Circuits is presented, investigating how it changes with respect to entanglement topology, adopted gates, and Quantum Machine Learning tasks to be performed. The objective of the analysis is to identify the optimal way to construct circuits for Quantum Neural Networks. In the presented experiments, two types of circuits are used: one with alternating layers of rotations and entanglement, and the other, similar to the first one, but with an additional final layer of rotations. As rotation layers, all combinations of one and two rotation sequences are considered. Four different entanglement topologies are compared: linear, circular, pairwise, and full. Different tasks are considered, namely the generation of probability distributions and images, and image classification. Achieved results are correlated with the expressibility and entanglement capability of the different circuits to understand how these features affect performance.

DOI
10.1109/qce65121.2025.10342

Related papers

The Effects of Conditional Pooling Techniques in Quanvolutional Circuits of Quantum-Classical Hybrid Neural Networks

Robin Faier, Jeanette Miriam Lorenz, Hans Ehm

This paper explores the performance of quantum-classical hybrid networks in image classification tasks, focusing on the integration of quantum circuits as alternative feature extractors to traditional convolutional layers. Specifically, it investigates the use of quanvolutional layers, variational quantum circuits that leverage quantum entanglement and quantum gates, in comparison to classical layers. The study examines various quantum pooling techniques, including conditional entanglement gates, and their impact on classification accuracy across datasets with varying complexity. By experimenting with different pooling strategies, both parametrized and non-parametrized, this work assesses their influence on network performance and feature representation. Results indicate that quanvolutional layers in a hybrid network consistently outperform classical convolutional layers in terms of classification accuracy, particularly when applied to datasets with prominent features. Additionally, the findings suggest that quantum entanglement techniques, rather than rotation parameters, play a more significant role in enhancing performance. This paper concludes that quantum-classical hybrid networks offer a promising approach for feature extraction, although the optimal configuration of pooling methods depends on the characteristics of the data. Future research could further explore the interplay between quantum circuits and different pooling strategies for more effective feature representation.

•Jun 2, 2026•0 citations

Enhanced Quantum Neural Networks: A Full Entanglement Scheme for Binary Classification

Bishwajeet Sahoo, Priyansh Singhal, Piyush Joshi

In this article, we introduce a novel approach for enhancing the performance of Quantum Neural Networks (QNNs) through variations in entanglement schemes within variational forms. Our main objective in this research is to improve the performance of QNNs, specifically focusing on their application in classification tasks. We tested three models, each with a different type of entanglement scheme: Linear Entanglement, Circular Entanglement, and our proposed Full Entanglement. Each model consisted of three layers of Ry parameterized gates. We used the Breast Cancer Wisconsin (Diagnostic) dataset and encoded it using the ZZ-feature Map. Then we used this encoded data for training our models and evaluated them based on metrics such as training and validation loss. Our experimental results demonstrate that increasing entanglement can mitigate overfitting, with the proposed Full Entanglement scheme showing the most promising results.

IEEE World Conference on Applied Intelligence and Computing (AIC)•Jul 27, 2024•1 citation

Quantum neural networks in practice: a comparative study with classical models from standard data sets to industrial images

Daniel Basilewitsch, João F. Bravo, Christian Tutschku +1 more

Abstract In this study, we compare the performance of randomized classical and quantum neural networks (NNs) as well as classical and quantum-classical hybrid convolutional neural networks (CNNs) for the task of supervised binary image classification. We keep the employed quantum circuits compatible with near-term quantum devices and use two distinct methodologies: applying randomized NNs on dimensionality-reduced data, and applying CNNs to full image data. We evaluate these approaches on three fully-classical data sets of increasing complexity: an artificial hypercube data set, MNIST handwritten digits and industrial images of practical relevance. Our study’s central goal is to shed more light on how quantum and classical models perform for various binary classification tasks and on what defines a good quantum model. To this end, our study involves a correlation analysis between classification accuracy and quantum model hyperparameters, and an analysis on the role of entanglement in quantum models, as well as on the impact of initial training parameters. We find classical and quantum-classical hybrid models achieve statistically-equivalent classification accuracies across most data sets with no approach consistently outperforming the other. Interestingly, we observe that quantum NNs show lower variance with respect to initial training parameters and that the role of entanglement is nuanced. While incorporating entangling gates seems to be generally advantageous, we also observe that their (optimizable) entangling power is not correlated with model performance. We also observe an inverse proportionality between the number of entangling gates and the average gate entangling power. Our study provides an industry perspective on quantum machine learning for practical binary image classification tasks, highlighting both current limitations and potential avenues for further research in quantum circuit design, entanglement utilization, and model transferability across varied applications.

arXiv:2411.19276•Quantum Machine Intelligence•Nov 20, 2025•5 citations

An Evaluation of Hardware-Efficient Quantum Neural Networks for Image Data Classification

Tuyen Nguyen, Incheon Paik, Yutaka Watanobe +1 more

Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by a classical computer. This hybrid approach can have the benefits of both quantum computing and classical machine learning methods. In this early stage, it is of crucial importance to understand the new characteristics of quantum neural networks for different machine learning tasks. In this paper, we will study quantum neural networks for the task of classifying images, which are high-dimensional spatial data. In contrast to previous evaluations of low-dimensional or scalar data, we will investigate the impacts of practical encoding types, circuit depth, bias term, and readout on classification performance on the popular MNIST image dataset. Various interesting findings on learning behaviors of different QNNs are obtained through experimental results. To the best of our knowledge, this is the first work that considers various QNN aspects for image data.

Electronics•Feb 1, 2022•26 citations

Performance analysis of quantum convolutional layers for image classification

Qiuyu Xiang, Dongfen Li, Yuchen Sun +6 more

Abstract In recent years, with the rapid development of quantum computing technology, the fusion of quantum computing and machine learning techniques is becoming a research hotspot in the field of machine learning. This article aims to explore the impact of the depth and width of quantum convolutional layers on image classification tasks in Quantum−Classical Hybrid Convolutional Neural Networks. To this end, a model combining parameterized quantum circuits and classical neural networks is designed, and a series of experiments are conducted on the MNIST dataset to assess the specific effects of different configurations of quantum convolutional layers on model performance. The research results indicate that simply increasing the depth or width of quantum convolutional layers does not guarantee performance improvement and sometimes may even lead to performance degradation. Therefore, when designing quantum convolutional layers, we should make reasonable choices based on the actual needs of the application scenarios. Finally, based on these findings, a multidimensional optimization strategy is proposed to enhance the overall performance of the model. The achievements of this research not only provide important guidance for the design and optimization of Quantum−Classical Hybrid Convolutional Neural Networks but also offer new research perspectives for researchers in the field of quantum machine learning.

Physica Scripta•Nov 22, 2024•2 citations

An Optimal Evaluation of Entanglement Depth in Variational Quantum Classifiers for Efficient Deployment of Machine Learning Applications

Aryan Sahni, Krishnesh Mathur, Yashvi Diya Singh +1 more

Variational Quantum Classifiers (VQCs) are a common class of hybrid quantum-classical models developed for nearterm quantum machine learning applications. Despite growing focus, it is still unclear how circuit design choices, particularly entanglement depth, affect learning performance and optimisation stability when compared to reliable classical baselines [1]. In this research, we provide a controlled empirical analysis of entanglement depth in VQCs for medical image classification utilizing pre-extracted deep feature representations. Under a predetermined experimental design, we assessed VQCs with no entanglement, single-layer entanglement, and two-layer interleaved entanglement with different training set sizes. Our findings demonstrate that in low-data regimes, increasing entanglement could negatively impact accuracy due to optimization instability [2] and may not always enhance performance. Since classical machine learning baselines consistently match or surpass quantum models, there is no performance advantage of variational quantum classifiers was observed under the evaluated experimental conditions and preprocessing constraints. These results emphasize the significance of thorough, comparative benchmarking for real-world use and draw attention to the expressivity optimization trade-offs inherent in variational quantum learning.

International Conference on Electronic Systems and Intelligent Computing (ICESIC)•Mar 13, 2026•0 citations
In this work, an analysis of the performance of different Variational Quantum Circuits is presented, investigating how it changes with respect to entanglement topology, adopted gates, and Quantum Machine Learning tasks to be performed. The objective of the analysis is to identify the optimal way to construct circuits for Quantum Neural Networks. In the presented experiments, two types of circuits are used: one with alternating layers of rotations and entanglement, and the other, similar to the first one, but with an additional final layer of rotations. As rotation layers, all combinations of one and two rotation sequences are considered. Four different entanglement topologies are compared: linear, circular, pairwise, and full. Different tasks are considered, namely the generation of probability distributions and images, and image classification. Achieved results are correlated with the expressibility and entanglement capability of the different circuits to understand how these features affect performance.
This paper explores the performance of quantum-classical hybrid networks in image classification tasks, focusing on the integration of quantum circuits as alternative feature extractors to traditional convolutional layers. Specifically, it investigates the use of quanvolutional layers, variational quantum circuits that leverage quantum entanglement and quantum gates, in comparison to classical layers. The study examines various quantum pooling techniques, including conditional entanglement gates, and their impact on classification accuracy across datasets with varying complexity. By experimenting with different pooling strategies, both parametrized and non-parametrized, this work assesses their influence on network performance and feature representation. Results indicate that quanvolutional layers in a hybrid network consistently outperform classical convolutional layers in terms of classification accuracy, particularly when applied to datasets with prominent features. Additionally, the findings suggest that quantum entanglement techniques, rather than rotation parameters, play a more significant role in enhancing performance. This paper concludes that quantum-classical hybrid networks offer a promising approach for feature extraction, although the optimal configuration of pooling methods depends on the characteristics of the data. Future research could further explore the interplay between quantum circuits and different pooling strategies for more effective feature representation.
In this article, we introduce a novel approach for enhancing the performance of Quantum Neural Networks (QNNs) through variations in entanglement schemes within variational forms. Our main objective in this research is to improve the performance of QNNs, specifically focusing on their application in classification tasks. We tested three models, each with a different type of entanglement scheme: Linear Entanglement, Circular Entanglement, and our proposed Full Entanglement. Each model consisted of three layers of Ry parameterized gates. We used the Breast Cancer Wisconsin (Diagnostic) dataset and encoded it using the ZZ-feature Map. Then we used this encoded data for training our models and evaluated them based on metrics such as training and validation loss. Our experimental results demonstrate that increasing entanglement can mitigate overfitting, with the proposed Full Entanglement scheme showing the most promising results.
Abstract In this study, we compare the performance of randomized classical and quantum neural networks (NNs) as well as classical and quantum-classical hybrid convolutional neural networks (CNNs) for the task of supervised binary image classification. We keep the employed quantum circuits compatible with near-term quantum devices and use two distinct methodologies: applying randomized NNs on dimensionality-reduced data, and applying CNNs to full image data. We evaluate these approaches on three fully-classical data sets of increasing complexity: an artificial hypercube data set, MNIST handwritten digits and industrial images of practical relevance. Our study’s central goal is to shed more light on how quantum and classical models perform for various binary classification tasks and on what defines a good quantum model. To this end, our study involves a correlation analysis between classification accuracy and quantum model hyperparameters, and an analysis on the role of entanglement in quantum models, as well as on the impact of initial training parameters. We find classical and quantum-classical hybrid models achieve statistically-equivalent classification accuracies across most data sets with no approach consistently outperforming the other. Interestingly, we observe that quantum NNs show lower variance with respect to initial training parameters and that the role of entanglement is nuanced. While incorporating entangling gates seems to be generally advantageous, we also observe that their (optimizable) entangling power is not correlated with model performance. We also observe an inverse proportionality between the number of entangling gates and the average gate entangling power. Our study provides an industry perspective on quantum machine learning for practical binary image classification tasks, highlighting both current limitations and potential avenues for further research in quantum circuit design, entanglement utilization, and model transferability across varied applications.
Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by a classical computer. This hybrid approach can have the benefits of both quantum computing and classical machine learning methods. In this early stage, it is of crucial importance to understand the new characteristics of quantum neural networks for different machine learning tasks. In this paper, we will study quantum neural networks for the task of classifying images, which are high-dimensional spatial data. In contrast to previous evaluations of low-dimensional or scalar data, we will investigate the impacts of practical encoding types, circuit depth, bias term, and readout on classification performance on the popular MNIST image dataset. Various interesting findings on learning behaviors of different QNNs are obtained through experimental results. To the best of our knowledge, this is the first work that considers various QNN aspects for image data.
Abstract In recent years, with the rapid development of quantum computing technology, the fusion of quantum computing and machine learning techniques is becoming a research hotspot in the field of machine learning. This article aims to explore the impact of the depth and width of quantum convolutional layers on image classification tasks in Quantum−Classical Hybrid Convolutional Neural Networks. To this end, a model combining parameterized quantum circuits and classical neural networks is designed, and a series of experiments are conducted on the MNIST dataset to assess the specific effects of different configurations of quantum convolutional layers on model performance. The research results indicate that simply increasing the depth or width of quantum convolutional layers does not guarantee performance improvement and sometimes may even lead to performance degradation. Therefore, when designing quantum convolutional layers, we should make reasonable choices based on the actual needs of the application scenarios. Finally, based on these findings, a multidimensional optimization strategy is proposed to enhance the overall performance of the model. The achievements of this research not only provide important guidance for the design and optimization of Quantum−Classical Hybrid Convolutional Neural Networks but also offer new research perspectives for researchers in the field of quantum machine learning.
Variational Quantum Classifiers (VQCs) are a common class of hybrid quantum-classical models developed for nearterm quantum machine learning applications. Despite growing focus, it is still unclear how circuit design choices, particularly entanglement depth, affect learning performance and optimisation stability when compared to reliable classical baselines [1]. In this research, we provide a controlled empirical analysis of entanglement depth in VQCs for medical image classification utilizing pre-extracted deep feature representations. Under a predetermined experimental design, we assessed VQCs with no entanglement, single-layer entanglement, and two-layer interleaved entanglement with different training set sizes. Our findings demonstrate that in low-data regimes, increasing entanglement could negatively impact accuracy due to optimization instability [2] and may not always enhance performance. Since classical machine learning baselines consistently match or surpass quantum models, there is no performance advantage of variational quantum classifiers was observed under the evaluated experimental conditions and preprocessing constraints. These results emphasize the significance of thorough, comparative benchmarking for real-world use and draw attention to the expressivity optimization trade-offs inherent in variational quantum learning.