Quantum entanglement plays a pivotal role in various quantum information processing tasks. However, a universal and effective way to detect entanglement structures is still lacking, especially for high-dimensional and multipartite quantum systems. Noticing the mathematical similarities between the common representations of many-body quantum states and the data structures of images, we are inspired to employ advanced computer vision technologies for data analysis. In this work, we propose a hybrid convolutional neural network--transformer model for both the classification of Greenberger-Horne-Zeilinger and $W$ states and the detection of various entanglement structures. By leveraging the feature-extraction capabilities of convolutional neural networks and the powerful modeling abilities of transformers, we not only can effectively reduce the time and computational resources required for the training process but can also obtain high detection accuracies. Through numerical simulation and physical verification, it is confirmed that our hybrid model is more effective than traditional techniques and thus offers a powerful tool for characterizing multipartite entanglement structures.
Quantum entanglement plays a pivotal role in various quantum information processing tasks. However, a universal and effective way to detect entanglement structures is still lacking, especially for high-dimensional and multipartite quantum systems. Noticing the mathematical similarities between the common representations of many-body quantum states and the data structures of images, we are inspired to employ advanced computer vision technologies for data analysis. In this work, we propose a hybrid convolutional neural network--transformer model for both the classification of Greenberger-Horne-Zeilinger and W states and the detection of various entanglement structures. By leveraging the feature-extraction capabilities of convolutional neural networks and the powerful modeling abilities of transformers, we not only can effectively reduce the time and computational resources required for the training process but can also obtain high detection accuracies. Through numerical simulation and physical verification, it is confirmed that our hybrid model is more effective than traditional techniques and thus offers a powerful tool for characterizing multipartite entanglement structures.
Abstract Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.
The emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.
Convolutional neural networks (CNNs) are extensively employed for tasks such as image processing, employing a hierarchical feature extraction approach facilitated by convolutional layers. A recent trend explores quantum counterparts called Quanvolutional layers, utilizing random quantum circuits akin to convolutional filter layers. Our work advances this by introducing structured entanglement in quanvolutional layers using Basic and Strongly Entangled Layers. Unlike random circuit approaches, these layers have defined structures and high entanglement, requiring minimal error correction. We evaluated their performance on MNIST dataset, comparing various techniques like traditional CNNs, Quanvolutional Neural Networks (QNNs) with Random quantum circuits, basic entangled layers, strongly entangled layers. Structured entanglement shows promise in improving feature extraction for QNNs, providing insights into their understanding. Our work explores the use of structures quantum layers in constructing quantum-classical hybrid architectures and evaluates their performance. Future research may explore scalability and applicability in complex datasets and integration with other quantum computing techniques for enhanced performance and versatility.
Accurate classification of multipartite entanglement in high-dimensional quantum systems is crucial for advancing quantum communication and information processing. However, conventional methods are resource-intensive, and even many machine-learning-based approaches necessitate large training datasets, creating a significant experimental bottleneck for data acquisition. To address this challenge, we propose a hybrid neural network architecture integrating Convolutional and Bidirectional Long Short-Term Memory networks (CNN-BiLSTM). This design leverages CNNs for local feature extraction and BiLSTMs for sequential dependency modeling, enabling robust feature learning from minimal training data. We investigate two fusion paradigms: Architecture 1 (flattening-based) and Architecture 2 (dimensionality-transforming). When trained on only 100 samples, Architecture 2 maintains classification accuracies exceeding 90% for both 3-qubit and 4-qubit systems, demonstrating rapid loss convergence within tens of epochs. Under full-data conditions (400 000 samples), both architectures achieve accuracies above 99.97%. Comparative benchmarks reveal that our CNN-BiLSTM models, especially Architecture 2, consistently outperform standalone CNNs, BiLSTMs, and MLPs in low-data regimes, albeit with increased training time. These results demonstrates that the tailored CNN-BiLSTM fusion significantly alleviates experimental data acquisition burden, offering a practical pathway toward scalable entanglement verification in complex quantum systems.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
Diabetic Retinopathy (DR) is a severe complication of diabetes, characterized by damage to the blood vessels in the retina, leading to an increased risk of vision loss or blindness. Patients with diabetes who develop DR face a significant risk of visual impairment, underscoring the need for advanced and reliable diagnostic tools. This research addresses this need by introducing a hybrid Quantum Convolutional Neural Network (QCNN) model, designed specifically for the automated detection of DR. By leveraging quantum principles and classical deep learning, this model provides a powerful approach to screening for diabetic retinopathy. In a Convolutional Neural Network (CNN), convolutional layers are primarily responsible for feature extraction from images, capturing intricate details essential for accurate classification. However, the proposed hybrid QCNN introduces quantum layers in addition to traditional convolutional layers, combining the strengths of both quantum computing and classical neural network structures. Quantum computing principles, such as superposition and entanglement, allow the QCNN to encode and process information in unique ways, enhancing the efficiency of feature extraction and the overall performance of the model. Superposition enables the QCNN to represent and manipulate multiple states simultaneously, while entanglement allows for a more complex, interconnected representation of image features. These quantum properties enable the QCNN to explore a broader feature space, offering richer representations of the retinal images than those achievable through classical methods alone. Evaluation of the model’s performance reveals that the QCNN surpasses a traditional CNN in both accuracy and diagnostic performance. This improvement suggests that hybrid QCNN models hold significant promise for enhancing diabetic retinopathy screening, paving the way for earlier diagnosis and better patient outcomes. Future research will focus on refining the hybrid QCNN model to improve its robustness and adaptability, aiming to ensure its effectiveness in real-world healthcare environments.