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:2406.03081 · Physica Scripta

A quantum neural network-based approach to power quality disturbances detection and recognition

Guodong Li, Haiyan He, Yue Li, Xinhao Li, Hao Liu, Qingle Wang, Long Cheng

Published Jun 2, 2025• 7 citations
arXivHTMLPDF

Abstract

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.

DOI
10.1088/1402-4896/adda9e

Related papers

An advanced quantum support vector machine for power quality disturbance detection and identification

Qingle Wang, Jin Zhong Yu, Xinhao Li +5 more

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of $O(N^{2} \log (N))$ , superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.

EPJ Quantum Technology•Oct 22, 2024•19 citations

Exploring Quantum Neural Networks: Enhancing AI with Quantum Computing Power

Eleanor WHITMORE

A promising combination of artificial intelligence and quantum computing, quantum neural networks (QNNs) have the potential to completely transform the way neural networks interpret and learn from data. QNNs can outperform classical neural networks in terms of speed, efficiency, and scalability by utilizing the special qualities of quantum physics, such as superposition, entanglement, and quantum parallelism. The basic ideas of QNNs, their structures, and the ways in which quantum algorithms can be incorporated into machine learning processes to improve problem-solving skills are all examined in this study. current developments in QNNs, emphasizing how they can be used for challenging applications including pattern recognition, optimization, and high-dimensional data space decision-making. We also go over the difficulties in creating scalable QNN models and the possible future applications of QNNs in artificial intelligence. By utilizing the potential of quantum computing, QNNs have the potential to advance artificial intelligence by providing answers to issues that are now intractable through traditional methods.

The Sankalpa: International Journal of Management Decisions•Jun 28, 2026•0 citations

Variational Quantum Neural Networks (VQNNS) in Image Classification

Meghashrita Das, Tirupati Bolisetti

Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are able to solve problems with complex correlations between inputs that can be hard for classical computers. This suggests that learning models made on quantum computers may be more powerful for applications, potentially faster computation and better generalization on less data. The objective of this paper is to investigate how training of quantum neural network (QNNs) can be done using quantum optimization algorithms for improving the performance and time complexity of QNNs. A classical neural network can be partially quantized to create a hybrid quantum-classical neural network which is used mainly in classification and image recognition. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs). We encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters. The parameters are tuned with an iterative quantum approximate optimisation (QAOA) mixer and problem hamiltonians. VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets (more complex) which converges the computation in lesser time than QNN with decent training accuracy.

arXiv:2303.05860•Mar 10, 2023•6 citations

Coherent feed-forward quantum neural network

Utkarsh Singh, Aaron Z. Goldberg, Khabat Heshami

Abstract Quantum machine learning, focusing on quantum neural networks (QNNs), remains a vastly uncharted field of study. Current QNN models primarily employ variational circuits on an ansatz or a quantum feature map, often requiring multiple entanglement layers. This methodology not only increases the computational cost of the circuit beyond what is practical on near-term quantum devices but also misleadingly labels these models as neural networks, given their divergence from the structure of a typical feed-forward neural network (FFNN). Moreover, the circuit depth and qubit needs of these models scale poorly with the number of data features, resulting in an efficiency challenge for real-world machine learning tasks. We introduce a bona fide QNN model, which seamlessly aligns with the versatility of a traditional FFNN in terms of its adaptable intermediate layers and nodes, absent from intermediate measurements such that our entire model is coherent. This model stands out with its reduced circuit depth and number of requisite CNOT gates, achieving a more than 50% reduction in both compared to prevailing QNN models. Furthermore, the qubit count in our model remains unaffected by the data’s feature quantity. We test our proposed model on various benchmarking datasets such as the breast cancer diagnostic (Wisconsin) and credit card fraud detection datasets. Our model achieved an accuracy of 91% on the breast cancer dataset and 85% on the credit card fraud detection dataset, outperforming existing QNN methods by 5–10% while requiring approximately 50% fewer quantum resources. These results showcase the advantageous efficacy of our approach, paving the way for the application of quantum neural networks to relevant real-world machine learning problems.

arXiv:2402.00653•Quantum Machine Intelligence•Dec 1, 2024•4 citations

Quantum-Enhanced Hybrid Variational Neural Network for IoT Intrusion Detection

Aayushmaan Shrivastava, Nikhat Raza Khan, Ashish Singhal +3 more

Weaknesses arising from heterogeneous device structure, high-dimensional traffic patterns, and more complex nonlinear attack behaviors. The traditional intrusion detection systems (IDS), which rely on the classical machine learning models, often face the challenges of scalability, feature explosion, and low representational ability when handling large-scale IoT traffic data. To meet these challenges, we propose a Hybrid Quantum-Classical Variational Neural Network (HQCVNN) for effective intrusion detection in IoT systems. The model utilizes a Variational Quantum Circuit (VQC) as a nonlinear quantum feature map followed by dense neural layers for overall classification. Angle embedding with entanglement layers enables the encoding of quantum data and leads to a better separability of features in high-dimensional space, whilst the classical optimization guarantees efficient training on NISQ time scales. The model is implemented using Qiskit and evaluated on classical benchmark intrusion detection datasets. Experimental results show the proposed method has better classification accuracy, F1-score, and complexity reduction than conventional ANNs and SVMs. The results demonstrate the promise of hybrid quantum machine learning systems with end-to-end general-purpose capabilities in future IoT security and provide pragmatic impressions on how to deploy them on near-term quantum processors.

International Conference Big Data and Machine Learning•Feb 27, 2026•0 citations

Power Theft Detection in Smart Grids Using Hybrid Quantum Machine Learning with Variational Quantum Circuits, Data Reuploading, and Entanglement Layers

Chegudi Vijayababu, Dr. Krishna Mohan Ankala, Shaik Lateefa

Thefts of electricity are a plague on smarter grids and dispersed generation of energy from PV systems, because smart meters could be tampered with. The identification of theft at the DG side by utilising typical ML algorithms is hard to achieve and is not appropriate for high-dimensional imbalanced data. To solve these problems, a hybrid Quantum DL model is designed using the QVC and DRC method and applied by a QML technique. The QVC consists of an angle-embedding layer to encode the classical neural outputs into the qubits and a set of trainable parameters of rotational and CNOT gates for the noise reduction on the NISQ devices. If there is still noise, the DRC layer will re-upload the data iteratively to compensate for it. The data set 'Smart Grid Theft Detection' is retrieved from Kaggle and the features in it are divided into 80% training and 20% testing sets. The FH-QVC-DRC model has an accuracy of 88% which is higher than standard algorithms like the XGBOOST and LIGHTGBM with 86% and 73% accuracy, respectively. Furthermore, the Entanglement quantum layer is proposed to boost the performance by using the quantum parallelism, quantum interference and secure state correlations. The developed model is called FH-QVC-DRC-ENT with an accuracy of 91 %. The visualisation, the performance indicators and the prediction output demonstrate that this quantum model, which uses entanglement, is able to better classify events of theft and non-theft, than the other models.

International Journal of Engineering Research and Science & Technology•Jul 28, 2026•0 citations
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.
Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O(N2log⁡(N))O(N^{2} \log (N))O(N2log(N)) , superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.
A promising combination of artificial intelligence and quantum computing, quantum neural networks (QNNs) have the potential to completely transform the way neural networks interpret and learn from data. QNNs can outperform classical neural networks in terms of speed, efficiency, and scalability by utilizing the special qualities of quantum physics, such as superposition, entanglement, and quantum parallelism. The basic ideas of QNNs, their structures, and the ways in which quantum algorithms can be incorporated into machine learning processes to improve problem-solving skills are all examined in this study. current developments in QNNs, emphasizing how they can be used for challenging applications including pattern recognition, optimization, and high-dimensional data space decision-making. We also go over the difficulties in creating scalable QNN models and the possible future applications of QNNs in artificial intelligence. By utilizing the potential of quantum computing, QNNs have the potential to advance artificial intelligence by providing answers to issues that are now intractable through traditional methods.
Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are able to solve problems with complex correlations between inputs that can be hard for classical computers. This suggests that learning models made on quantum computers may be more powerful for applications, potentially faster computation and better generalization on less data. The objective of this paper is to investigate how training of quantum neural network (QNNs) can be done using quantum optimization algorithms for improving the performance and time complexity of QNNs. A classical neural network can be partially quantized to create a hybrid quantum-classical neural network which is used mainly in classification and image recognition. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs). We encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters. The parameters are tuned with an iterative quantum approximate optimisation (QAOA) mixer and problem hamiltonians. VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets (more complex) which converges the computation in lesser time than QNN with decent training accuracy.
Abstract Quantum machine learning, focusing on quantum neural networks (QNNs), remains a vastly uncharted field of study. Current QNN models primarily employ variational circuits on an ansatz or a quantum feature map, often requiring multiple entanglement layers. This methodology not only increases the computational cost of the circuit beyond what is practical on near-term quantum devices but also misleadingly labels these models as neural networks, given their divergence from the structure of a typical feed-forward neural network (FFNN). Moreover, the circuit depth and qubit needs of these models scale poorly with the number of data features, resulting in an efficiency challenge for real-world machine learning tasks. We introduce a bona fide QNN model, which seamlessly aligns with the versatility of a traditional FFNN in terms of its adaptable intermediate layers and nodes, absent from intermediate measurements such that our entire model is coherent. This model stands out with its reduced circuit depth and number of requisite CNOT gates, achieving a more than 50% reduction in both compared to prevailing QNN models. Furthermore, the qubit count in our model remains unaffected by the data’s feature quantity. We test our proposed model on various benchmarking datasets such as the breast cancer diagnostic (Wisconsin) and credit card fraud detection datasets. Our model achieved an accuracy of 91% on the breast cancer dataset and 85% on the credit card fraud detection dataset, outperforming existing QNN methods by 5–10% while requiring approximately 50% fewer quantum resources. These results showcase the advantageous efficacy of our approach, paving the way for the application of quantum neural networks to relevant real-world machine learning problems.
Weaknesses arising from heterogeneous device structure, high-dimensional traffic patterns, and more complex nonlinear attack behaviors. The traditional intrusion detection systems (IDS), which rely on the classical machine learning models, often face the challenges of scalability, feature explosion, and low representational ability when handling large-scale IoT traffic data. To meet these challenges, we propose a Hybrid Quantum-Classical Variational Neural Network (HQCVNN) for effective intrusion detection in IoT systems. The model utilizes a Variational Quantum Circuit (VQC) as a nonlinear quantum feature map followed by dense neural layers for overall classification. Angle embedding with entanglement layers enables the encoding of quantum data and leads to a better separability of features in high-dimensional space, whilst the classical optimization guarantees efficient training on NISQ time scales. The model is implemented using Qiskit and evaluated on classical benchmark intrusion detection datasets. Experimental results show the proposed method has better classification accuracy, F1-score, and complexity reduction than conventional ANNs and SVMs. The results demonstrate the promise of hybrid quantum machine learning systems with end-to-end general-purpose capabilities in future IoT security and provide pragmatic impressions on how to deploy them on near-term quantum processors.
Thefts of electricity are a plague on smarter grids and dispersed generation of energy from PV systems, because smart meters could be tampered with. The identification of theft at the DG side by utilising typical ML algorithms is hard to achieve and is not appropriate for high-dimensional imbalanced data. To solve these problems, a hybrid Quantum DL model is designed using the QVC and DRC method and applied by a QML technique. The QVC consists of an angle-embedding layer to encode the classical neural outputs into the qubits and a set of trainable parameters of rotational and CNOT gates for the noise reduction on the NISQ devices. If there is still noise, the DRC layer will re-upload the data iteratively to compensate for it. The data set 'Smart Grid Theft Detection' is retrieved from Kaggle and the features in it are divided into 80% training and 20% testing sets. The FH-QVC-DRC model has an accuracy of 88% which is higher than standard algorithms like the XGBOOST and LIGHTGBM with 86% and 73% accuracy, respectively. Furthermore, the Entanglement quantum layer is proposed to boost the performance by using the quantum parallelism, quantum interference and secure state correlations. The developed model is called FH-QVC-DRC-ENT with an accuracy of 91 %. The visualisation, the performance indicators and the prediction output demonstrate that this quantum model, which uses entanglement, is able to better classify events of theft and non-theft, than the other models.