An improved variational quantum shadow learning (VQSL) method is presented for the fault diagnosis of transformers. Localized variational quantum circuits for shadow feature extraction and a fully connected neural network are designed. Firstly, the dissolved gas analysis data is mapped into a high-dimensional Hilbert space, then, shadow features between adjacent and non-adjacent quantum bits are extracted simultaneously by multiple localized quantum circuits through parameter sharing, which reduces the number of training parameters and improves the model accuracy. The key factors that influence the performance of the VQSL method are discussed, which include quantum coding methods, the width and the depth of localized quantum circuits, the size of the global quantum bits, and the noise levels. Experimental results indicate that the improved VQSL method achieves a diagnostic accuracy of 95.4%, surpassing the commonly used methods such as QNN, GNN, CNN, SAE, SVM, and ratio methods. Even in a noise interference situation, the proposed method consistently maintains a high fault diagnosis accuracy.
An improved variational quantum shadow learning (VQSL) method is presented for the fault diagnosis of transformers. Localized variational quantum circuits for shadow feature extraction and a fully connected neural network are designed. Firstly, the dissolved gas analysis data is mapped into a high-dimensional Hilbert space, then, shadow features between adjacent and non-adjacent quantum bits are extracted simultaneously by multiple localized quantum circuits through parameter sharing, which reduces the number of training parameters and improves the model accuracy. The key factors that influence the performance of the VQSL method are discussed, which include quantum coding methods, the width and the depth of localized quantum circuits, the size of the global quantum bits, and the noise levels. Experimental results indicate that the improved VQSL method achieves a diagnostic accuracy of 95.4%, surpassing the commonly used methods such as QNN, GNN, CNN, SAE, SVM, and ratio methods. Even in a noise interference situation, the proposed method consistently maintains a high fault diagnosis accuracy.
Early transformer fault diagnosis is challenged by nonlinear dissolved-gas interactions, overlapping fault signatures, and limited labeled data, while practical deployment further requires reliable performance under realistic computational constraints. This paper presents a simulation-driven modeling framework for dissolved gas analysis-based transformer fault diagnosis, in which a carefully engineered variational quantum classifier (VQC) is employed as the computational core and systematically analyzed through simulation. The framework integrates domain-aware feature modeling derived from Duval geometry with a lightweight two-qubit quantum representation, enabling nonlinear gas-interaction effects to be captured within a shallow parameterized circuit. A hybrid ZX-YY quantum feature map is designed to model non-commuting feature interactions, while a full-entanglement EfficientSU2 ansatz provides adequate expressive capacity under strict resource limits. Model behavior is evaluated using a comprehensive simulation pipeline including noise-aware circuit emulation, cross-dataset validation, and limited hardware-in-the-loop execution, allowing key effects of circuit depth, noise, and optimization strategy to be examined. Simulation results on benchmark dissolved-gas-analysis datasets demonstrate high diagnostic accuracy, strong generalization capability, and robustness to realistic noise levels with minimal quantum resources. The results highlight the effectiveness of simulation-informed modeling for practical transformer diagnostic applications, offering a reproducible and resource-efficient pathway for evaluating quantum-enhanced fault diagnosis methods.
Abstract Convolutional neural network (CNN), as a prominent machine learning model, is crucial in image classification and feature extraction by completely utilizing the correlation information of data. Currently, the CNN is facing the challenges of slow computing speed, expanding data scale and poor interpretation for rapidly increasing Hilbert space. Fortunately, quantum convolutional neural network (QCNN) promises an elegant solution to improve the efficiency of data processing by combining the superiority of quantum computing and the feature extraction capability of the CNN. However, most of the existing QCNNs have the problem that they are feeble to extract global features effectively and coincidentally variational shadow quantum circuit (VSQC) can preferably figure out it. Accordingly, an enhanced QCNN assisted by the VSQC (VSQC‐QCNN) is developed. To be specific, the input data is primarily preprocessed by the VSQC module to establish global entanglement and extract effective features. Then the output shadow features are further processed by encoding, quantum circuit evolution and decoding through quantum convolutional layer and the dimensionality of the feature results is reduced through the pooling layer. When the data dimension is small enough, it is fed into a fully connected neural network to make classification predictions via activation functions. Experiments conducted on the MNIST dataset through the VQNet quantum framework demonstrate that the VSQC‐QCNN model is with performance surpassing the original QCNN in terms of loss function, classification accuracy and training speed. In addition, it is proved that the VSQC‐QCNN model has good astringency and stability under different circuit depths, widths and learning rates. Remarkably, the classification average accuracy of the model can reach up to 99% (even 100% for a few cases). Finally, experiments are also performed on Fashion‐MNIST and Iris datasets, indicating that the designed model has good universality.
Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shadows of quantum data, which fundamentally represent the side information of quantum data with respect to certain physical observables. Specifically, we first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task. We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training. Simultaneously, less noise will be introduced since fewer quantum gates are employed in such shadow circuits. Moreover, we show that the Barren Plateau issue, a significant gradient vanishing problem in quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. In particular, our VSQL approach outperforms existing variational quantum classifiers in the test accuracy in the binary case of handwritten digit recognition and notably requires much fewer parameters.
In order to explore the possibility of cross-fertilization between quantum computing and neural networks as well as to improve the classification performance of quantum neural networks, this paper proposes an improved Variable Split Shadow Quantum Circuit (VSQC-WOA) model based on the Whale Optimization Algorithm. In this model, we design a strongly entangled local shadow circuit to achieve efficient characterization of global features through local shadow feature extraction and a sliding mechanism, which provides a rich quantum feature representation for the classification task. The gradient is then computed by the parameter-shifting method, and finally the features processed by the shadow circuit are passed to the classical fully connected neural network (FCNN) for processing and classification. The model also introduces the Whale Optimization Algorithm (WOA) to further optimize the weights and biases of the fully connected neural network, which improves the expressive power and classification accuracy of the model. In this paper, we firstly use different localized shadow circuit VSQC models to achieve the binary classification task on the MNIST dataset, and our design of strongly entangled shadow circuits performs the best in terms of classification accuracy. The VSQC-WOA model is then used to multi-classify the MNIST dataset (three classifications as an example), and the effectiveness of the proposed VSQC-WOA model as well as the robustness and generalization ability of the model are verified through various comparison experiments.
Heart disease is one of the major global causes of death, requiring early and precise detection for efficient treatment.With advancements in quantum computing, Variational Quantum Circuits (VQCs) have gained recognition as a valuable technique for improving learning models in medical diagnosis.This study further explores the application of VQC-based models for heart disease prediction, leveraging quantum computing to enhance classification accuracy.A hybrid quantum-classical model has been developed, which combines classical preprocessing for feature extraction from patient datasets with a VQC-based classifier for medical predictions.The quantum circuit using variational parameters is optimized to reduce the loss function and facilitate effective learning from complex medical data.This proposed approach is tested on benchmark heart disease datasets and evaluated against classical Machine Learning (ML) models, including Support Vector Machines (SVMs), Random Forests, and Neural Networks.The experimental results show that VQC-based models achieve high accuracy and outperform classical models, notably on high-dimensional, non-linearly separable data.The quantum advantage of VQCs lies in their ability to encode and process data in a huge Hilbert space.However, challenges such as noise in near-term quantum devices and scalability issues remain key.This research underscores the efficacy of VQCbased quantum machine learning in medical diagnosis, paving the way for future studies on quantum-enhanced healthcare solutions.Further advancements in quantum hardware and hybrid learning techniques could allow quantum-assisted heart disease prediction to become an effective clinical tool.
Abstract As an emerging technology force, quantum algorithms have shown great potential and unique advantages in many fields of application. Power quality disturbances (PQDs) affect the security and stability of the power system, which may lead to equipment damage, system failures and economic losses. The accurate detection and recognition of PQDs is a guarantee for the safe operation of the system. In order to explore the application of quantum algorithms in the field of power quality, an improved quantum neural network(QNN) is proposed for PQDs detection and recognition. The QNN model consists of three parts: quantum encoding, quantum variational, and quantum measurement. Firstly, the classical data is mapped to the high-dimensional quantum feature space through the encoding layer, so that it is transformed into quantum data. Subsequently, a unique quantum circuit is constructed in the quantum variational layer to adjust the rotation angles and entanglement of data and ancilla qubits to realise quantum information transformation. The quantum information obtains the expected value through the measurement layer, which is used for model optimization and subsequent disturbance classification. The experimental results show that the detection accuracy of PQDs by this method reaches 99.75%, the recognition accuracy of single disturbance and mixed disturbance reaches 97.85% and 95.50%, respectively. The model uses fewer parameters to obtain high accuracy under the same conditions compared to classical machine learning models. In addition, comparative tests in different noise environments show the robustness of the model.