A particle generated inside a group cannot be described independently of the other group particles during the interaction, sharing, or the development of a particle's group in close geographic in the quantum mechanics. Particles' arrangement represents their quantum states at zero temperature and highlights the particle's energy and symmetry gap during the Symmetry Protected Topology SPT as the “trivial” topological instructions' existence of these particles and provides a more consistent method for differentiating the quantum data source's features such as a quantum sensor or an intricate simulation of a device through quantum entanglement. In this research utilizing the TensorFlow framework with a cluster state as a data source which mayor may not excited, the researcher demonstrates and evaluates a quantum variant of the conventional classical neural network (CNN) called the hybrid quantum-classical convolutional neural network (HQCNN) which is trained on data containing a quantum mechanical state protected by the SPT topology. The accuracy was determined in purely model's results did not exceed 89.58% accuracy within ten epochs, whereas the hybrid model's results were accurate to 100% within three epochs with a single (Twist) filter and seven epochs with a multi-filter (multi-Twist) model with the goal of eliminating quantum entanglement.
A particle generated inside a group cannot be described independently of the other group particles during the interaction, sharing, or the development of a particle's group in close geographic in the quantum mechanics. Particles' arrangement represents their quantum states at zero temperature and highlights the particle's energy and symmetry gap during the Symmetry Protected Topology SPT as the “trivial” topological instructions' existence of these particles and provides a more consistent method for differentiating the quantum data source's features such as a quantum sensor or an intricate simulation of a device through quantum entanglement. In this research utilizing the TensorFlow framework with a cluster state as a data source which mayor may not excited, the researcher demonstrates and evaluates a quantum variant of the conventional classical neural network (CNN) called the hybrid quantum-classical convolutional neural network (HQCNN) which is trained on data containing a quantum mechanical state protected by the SPT topology. The accuracy was determined in purely model's results did not exceed 89.58% accuracy within ten epochs, whereas the hybrid model's results were accurate to 100% within three epochs with a single (Twist) filter and seven epochs with a multi-filter (multi-Twist) model with the goal of eliminating quantum entanglement.
In this research, a Hybrid Quantum Convolutional Neural Network (HQCNN) was proposed for Symmetry-Protected Topological (SPT) Phase data Classification. The HQCNN used quantum Convolutional and pooling layers with traditional fully connected networks. The quantum Convolutional (QConv) and quantum pooling (QPool) layers were developed using quantum circuit states. The TensorFlow quantum package was used to add the outcomes of the quantum circuits to dense layers for efficient classification. A different number of quantum filters were used in the HQCNN model and tested. A different number of quantum filters were used in the HQCNN model design. The performance of the HQCNN models with the different number of quantum filters was compared using validation and testing performance. The simulation results showed that the classification accuracy of the proposed HQCNN with Four Quantum Filters (HQCNN-4QF) was superior to other proposed HQCNNs on the SPT phase data Classification. The HQCNN-4QF model achieved the classification accuracy of 99.22 on SPT phase test data.
The Search for Extraterrestrial Intelligence Institute (SETI) was established to gather and analyze radio signals from space in an effort to detect signs of advanced civilizations that may possibly exist elsewhere in the cosmos. A two-dimensional spectrogram is created using time-series raw data obtained from the Allen telescope. Rigorous data analysis has revealed that the SETI image data set is highly non-separable and challenging. In this paper, we suggest the use of a Hybrid Quantum-Classical convolutional Neural network (HQCNN) using a Quantum node serving to extract intricate features of SETI radio images that are being classified with classical convolutional Neural Network. We also generalized the combination of quantum gates in the Quantum node to improvise the performance of HQCNN. Quantum superposition and entanglement properties of Quantum computers render it feasible to comprehend the basic relationships in the data set more precisely than with traditional Machine Learning and Deep Learning techniques. In this context, with the notion of leveraging the computational benefit of an HQCNN, we, therefore, treated this challenging task as binary classification and filtered any two classes from the primary data set. The experimental analysis was executed in the Google Collaboratory platform with the Python packages such as Pennylane, Keras, and TensorFlow. To validate the outcomes, the HQCNN model is contrasted with various classical Deep learning models in terms of diverse performance metrics. The proposed model outperforms the other models in categorizing Allen Telescope data with the highest accuracy of 90.19% while utilizing a very small portion of the original data set, which motivates further research into the use of Quantum Machine Learning algorithms for signal processing tasks.
Classification of medical images plays a vital role in medical image analysis; however, it remains challenging due to the limited availability of labeled data, class imbalances, and the complexity of medical patterns. To overcome these challenges, we propose a novel Hybrid Quantum-Classical Neural Network (HQCNN) for both binary and multi-class classification. The architecture of HQCNN integrates a five-layer classical convolutional backbone with a 4-qubit variational quantum circuit that incorporates quantum state encoding, superpositional entanglement, and a Fourier-inspired quantum attention mechanism. We evaluate the model on six MedMNIST v2 benchmark datasets. The HQCNN consistently outperforms classical and quantum baselines, achieving up to 99.91% accuracy and 100.00% AUC on PathMNIST (binary) and 99.95% accuracy on OrganAMNIST (multi-class) with strong robustness on noisy datasets like BreastMNIST (87.18% accuracy). The model demonstrates superior generalization capability and computational efficiency, accomplished with significantly fewer trainable parameters, making it suitable for data-scarce scenarios. Our findings provide strong empirical evidence that hybrid quantum-classical models can advance medical imaging tasks.
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.
Abstract Quantum Neural Networks (QNNs) present a promising research direction in image recognition, demonstrating significant potential for various applications. However, QNNs with a fixed circuit topology structure are prone to encountering the barren plateaus during training when processing exponential number of qubits, which limit their performance and scalability. In this paper, a novel Hybrid Quantum-Classical Inception-inspired Convolutional Neural Networks (HQCNN) is proposed. The HQCNN enhances feature learning capability and mitigates the barren plateau issue by integrating quantum convolutional kernels with diverse circuit topologies across multiple channels. The architecture employs an amplitude encoding layer to map multi-channel classical data into quantum states, followed by diverse quantum convolutional kernels that extract feature representations. Furthermore, Hadamard gates are used to intergrate multi-channel features and incorporate quantum pooling to reduce feature dimensionality, with a fully connected layer mapping the quantum-enhancing features into class labels for classification tasks. Finally, by analyzing the influence of the circuit structures and quantum gate combinations on the models' performance, the optimal HQCNN architecture is constructed. Experimental results demonstrate that HQCNN achieves 98.5% accuracy on the MNIST-4 classification task and 92.5% accuracy on the MNIST-10 classification task, which indicates that the proposed model outperforms state-of-the-art HQCNN models and classical CNN in image classification with about 10% parameters. This study provides an effective approach for alleviating the barren plateau issue and designing multi-channel quantum convolutional neural networks for processing big classic data with fewer quantum resources.
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.