Abstract Rolling bearing is an indispensable part of the contemporary industrial system, and its working conditions affect the state of the entire industrial system. Therefore, there is great engineering value to researching and improving the fault diagnosis technology of rolling bearings. However, with the involvement of the whole mechanical equipment, we need to have a large quantity of data to support the accuracy of fault diagnosis, while the efficiency of classical machine learning algorithms is poor in processing big data, and huge amount of computing resources is required. To solve this problem, this paper combines the HHL algorithm in quantum computing with the LS-SVM algorithm in machine learning and proposes a fault diagnosis model based on a quantum least square support vector machine (QSVM). Based on experiments simulated on analog quantum computers, we demonstrate that our fault diagnosis based on QSVM is feasible, and it can play a far superior advantage over the classical algorithm in the context of big data.
Abstract Rolling bearing is an indispensable part of the contemporary industrial system, and its working conditions affect the state of the entire industrial system. Therefore, there is great engineering value to researching and improving the fault diagnosis technology of rolling bearings. However, with the involvement of the whole mechanical equipment, we need to have a large quantity of data to support the accuracy of fault diagnosis, while the efficiency of classical machine learning algorithms is poor in processing big data, and huge amount of computing resources is required. To solve this problem, this paper combines the HHL algorithm in quantum computing with the LS-SVM algorithm in machine learning and proposes a fault diagnosis model based on a quantum least square support vector machine (QSVM). Based on experiments simulated on analog quantum computers, we demonstrate that our fault diagnosis based on QSVM is feasible, and it can play a far superior advantage over the classical algorithm in the context of big data.
As a degenerative disease that seriously affects the central nervous system, early diagnosis of Parkinson's disease is important for slowing down the course of the disease and improving patients' quality of life.In recent years, machine learning has shown great potential in medical data analysis, and support vector machines (SVMs) are widely used in classification and prediction tasks.However, traditional SVMs have some limitations in high dimensionality and complex data processing.With the rapid development of quantum computing, quantum support vector machines (QSVMs) have become an emerging technology that has attracted much attention due to its ability to process largescale data and its potential for modeling high-dimensional features.In this paper, we compare traditional SVMs and QSVMs in the analysis of Parkinson's disease data, and evaluate their performance in terms of classification accuracy.The experiments are conducted on a publicly available dataset and analyzed in detail through the steps of model construction, data preprocessing, algorithm implementation, and performance evaluation.The results show that QSVM outperforms traditional SVM in complex feature space, which demonstrates its potential advantages and application prospects in medical data analysis.
Bearing fault diagnostics is essential for ensuring operational effectiveness and preventing failures across various industries, which often result in substantial financial losses. The development of quantum machine learning has introduced a paradigm shift in defect diagnostic methods, leveraging the computational power of quantum computers. This study investigates the impact of quantum computing and classical computing algorithms on Case Western Reserve University bearing dataset. The proposed methodology encompasses normalization, segmentation, data augmentation, and feature extraction/selection, executed on classical computer. Selected classical features are encoded as quantum state amplitudes, followed by the application of superposition and entanglement to qubits. The classification stage employs a quantum support vector machine, implemented, and simulated using the available IBM quantum resources. The results showcase 99.61% training accuracy and 99.14% testing accuracy with overall F1-score, recall, and precision of 99%. Comparative analysis between classical and quantum machine learning algorithms is conducted perfomace anlysis. These outcomes underscore the potential of quantum machine learning in defect diagnosis, offering heightened precision and reliability in practical applications with fast computing.
Quantum computing is an emerging field that can effectively solve several machine learning problems. The immensely growing data size has begun to create barriers for classical machine learning algorithms. Quantum computers proficiently handle and process big data composed of metrics and vectors. This paper presents the quantum machine learning model based on quantum support vector machines (QSVM) to classify brain tumours into malignant and benign. The dataset used in this experiment is Brats 2015, available on Kaggle. The QSVM-based model was run on real-time quantum machines and simulators to extract the performance metrics in terms of accuracy and execution time. The QSVM classification model is compared with its classical SVM equivalent. The kernel-based QSVM model implementation on a 32-qubit quantum simulator was 188 times faster with 95% accuracy which is 1.60% better than the classical equivalent. Similarly, the model takes 24.19% less time than the classical SVM model when realized on a 5-qubit real-time superconducting processor with the same accuracy. Hence, the results reveal that quantum machine learning models implemented on quantum computers outperformed their classical equivalents.
In Production Engineering, there is an area of research on reliability, maintenance, and risks in Production Systems. In this area, the objectives include maintenance policies aimed at predicting component failures and consequently minimizing unexpected downtimes of complex systems. Reliability engineering has successfully utilized machine learning to predict and categorize equipment and machine states. In this thesis, we aim to compare different machine learning algorithms and quantum machine learning in the context of reliability engineering. Specifically, we compare 8 models created with quantum machine learning, using three different bearing datasets: Case Western Reserve University, Machinery Failure Prevention Technology, and Paderborn University. These datasets mainly consist of accelerometer vibration data from the bearings, which must be processed to fit the different quantum machine learning algorithms. We focus on fault detection in rotating equipment, using quantum computing techniques to analyze sensor data installed on the equipment. Our approach considers the healthy state, inner ring faults, and outer ring faults of the bearings, with the aim of achieving fault detection. We compare established circuit designs, such as Real Amplitudes and Quantum Convolutional Circuits, as well as hybrid models that combine quantum circuits and neural networks using the library of TensorFlow with Cirq in python. We obtain results on classical computers using analytical calculations that simulate a quantum computer without experiencing the "quantum noise" delivered by the hardware. The most effective way to input data's key features is through a ZFeatureMap circuit, which assigns a qubit to each extracted feature from the data. Quantum convolutional circuits yield better results than other parameterized circuits, while hybrid models provide higher accuracy than their counterparts that do not utilize neural networks. The achieved accuracy rates on the training dataset are around 96%, suggesting that the parameterized quantum circuits used in this master's thesis yield stable results.
The current development of Support Vector Machines (SVM) has reached a bottleneck, with issues such as long training times and weak interpretability when dealing with large-scale, multi-dimensional data. This paper introduces the concept of Quantum Support Vector Machines (QSVM) and achieves efficient solutions through quantum algorithms such as the HHL algorithm. The research designs a computational architecture that combines classical and quantum computing, utilizing the Pauli decomposition of Hermitian matrices to simulate the quantum simulation of Hamiltonian quantities and implementing the quantum simulation of unitary operators. Based on the conclusions, a complete quantum linear solver circuit is designed, achieving exponential growth in computational complexity. Experiments using the Iris dataset demonstrate the excellent classification performance of QSVM, which outperform classical SVM in classification accuracy, computational time complexity, and memory requirements. More efficient quantum mapping algorithms and quantum circuit optimization methods are implemented, providing new ideas and methods for the application of SVM to large-scale datasets.
Quantum support vector machines (QSVMs) are a novel approach for solving classification problems, combining the principles of quantum computing with the support vector machine algorithm. In this study, we applied a QSVM to the Breast Cancer Wisconsin (Original) data set to classify tumors as benign or malignant. Our results showed that the QSVM outperformed classical support vector machines in terms of classification accuracy, achieving a success rate of 100% while classical SVM shows almost perfect result with its accuracy 95%. Additionally, the QSVM required significantly less training time compared to the classical SVM. These results demonstrate the potential of QSVMs for solving complex classification problems in the field of medical diagnosis.