Quantum imaging has the characteristics of non-locality and strong anti-interference ability, and it has received much attention. However, the target after entangled optical quantum imaging loses its local appearance structure, and it is difficult to be recognized accurately. Therefore, this paper proposes a denoise and target recognition method for entangled optical quantum imaging system. Specifically, we design the restore conditional generative adversarial network (RestoreCGAN) to restore and reconstruct the missing edge contour structure of the target. Then, we design the two-stream feature fusion convolutional neural network (TSFFCNet) to extract deep semantic features and shallow features for target recognition. The experimental results show that RestoreCGAN outperforms the state-of-the-art methods in terms of both PSNR and SSIM. Moreover, the recognition accuracy of the RestoreCGAN in combination with the TSFFCNet reaches 97.42%. This proves that the deep learning method is effective for denoise and target recognition of entangled optical quantum imaging system.
Quantum imaging has the characteristics of non-locality and strong anti-interference ability, and it has received much attention. However, the target after entangled optical quantum imaging loses its local appearance structure, and it is difficult to be recognized accurately. Therefore, this paper proposes a denoise and target recognition method for entangled optical quantum imaging system. Specifically, we design the restore conditional generative adversarial network (RestoreCGAN) to restore and reconstruct the missing edge contour structure of the target. Then, we design the two-stream feature fusion convolutional neural network (TSFFCNet) to extract deep semantic features and shallow features for target recognition. The experimental results show that RestoreCGAN outperforms the state-of-the-art methods in terms of both PSNR and SSIM. Moreover, the recognition accuracy of the RestoreCGAN in combination with the TSFFCNet reaches 97.42%. This proves that the deep learning method is effective for denoise and target recognition of entangled optical quantum imaging system.
To address the poor recognition performance problem that the target after entangled light quantum imaging loses local appearance structure, this paper proposes an entangled light quantum imaging method for target recognition based on denoising RestoreGAN. Specifically, we first prepare the entangled photon pairs via spontaneous parametric down-conversion to achieve quantum imaging. Then, we design a conditional generative adversarial network, named RestoreGAN, to restore the missing edge contour structure of the target. Finally, the denoised image is input into the designed feature extraction network for target recognition and classification. Tested on the MNIST, the experimental results demonstrate that our denoising RestoreGAN can improve target recognition accuracy by 3.92 percentages.
To address the problems of blurred contour and missed local image information of the entangled light quantum image, which leads to poor recognition performance, this paper proposes a target recognition method of entangled light quantum imaging based on a two-stream feature fusion network. Specifically, we first prepare the entangled photon pairs via spontaneous parametric down-conversion to achieve quantum imaging. Then, we design a two-stream feature fusion convolution network to recognize the target of the reconstructed quantum image. We test the MNIST handwritten digit images to verify the effectiveness of the proposed method.
In recent years, object detection technology has undergone substantial evolution within the realms of deep learning and computer vision, particularly owing to the extensive utilization of traditional convolutional neural networks (CNNs). These technologies perform well in a variety of applications, such as autonomous driving, security monitoring, and medical image analysis. However, traditional CNNS face many challenges when dealing with complex scenes and small target detection, such as background interference, target scale change and occlusion. In this paper, a novel Hybrid model, Hybrid Quantum-Cascade FPN, is proposed, which cleverly combines the advantages of Quantum convolutional neural network (QCNN) and classical CNN. The design of Hybrid quantum-Cascade FPN model is divided into three key stages: first, the Quantum coarse screening stage, which quickly extracts low-level features through quantum feature coding; Next is the quantum-classical feature fusion stage, which uses the parallelism of quantum computing and the structural advantages of classical networks to carry out multi-scale feature fusion. The last is the classical fine-tuning stage, which further optimizes the model to improve the high-level semantic analysis ability. Experiments on multiple data sets show that the model performs well in both detection accuracy and inference speed, especially in complex background and small target detection tasks.
In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag. The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.
We apply deep-neural-network-based techniques to quantum state classification and reconstruction. Our methods demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. Using optical quantum states as examples, we first demonstrate how convolutional neural networks (CNNs) can successfully classify several types of states distorted by, e.g., additive Gaussian noise or photon loss. We further show that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection. Secondly, we demonstrate reconstruction of quantum-state density matrices using neural networks that incorporate quantum-physics knowledge. The knowledge is implemented as custom neural-network layers that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states. Any standard feed-forward neural-network architecture can be adapted for quantum state tomography (QST) with our method. We present further demonstrations of our proposed QST technique with conditional generative adversarial networks (QST-CGAN) [Ahmed et al., Phys. Rev. Lett. 127, 140502 (2021)]. We motivate our choice of a learnable loss function within an adversarial framework by demonstrating that the QST-CGAN outperforms, across a range of scenarios, generative networks trained with standard loss functions. For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state. The QST-CGAN reconstructs states using up to two orders of magnitude fewer iterative steps than iterative and accelerated projected-gradient-based maximum-likelihood estimation (MLE) methods. We also demonstrate that the QST-CGAN can reconstruct both pure and mixed states from two orders of magnitude fewer randomly chosen data points than these MLE methods. Our paper opens possibilities to use state-of-the-art deep-learning methods for quantum state classification and reconstruction under various types of noise.
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.