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.
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.
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.
Breast cancer is a major health concern, and early detection can make a great difference in treatment and survival rates for breast cancer patients. Machine learning methods have noticeably improved prediction accuracy on high-dimensional medical datasets and have become widely used tools in medical diagnostics. The transition to a quantum computational framework has opened new directions in machine learning research. By using qubits instead of classical bits, quantum models may provide new ways to represent and process complex medical data. In medical diagnostics, this has led to a growing interest in whether quantum approaches can improve classification performance more effectively than classical methods. The purpose of this paper is to compare classical and quantum machine learning models on the task of breast cancer classification and to determine whether quantum models can achieve higher accuracy and faster prediction on a selected dataset. Alongside the main simulator-based experiment, a smaller experiment was performed on a real IBM quantum computer to demonstrate the practical execution of the same task under current hardware constraints. In the practical part of the study, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used. This dataset consists of 569 samples with 30 numerical features extracted from digitized fine needle aspirate images and provides a reliable basis for evaluating model performance. A comparative analysis was conducted between classical and quantum machine learning models, including a support vector machine (SVM), an artificial neural network (ANN), a quantum support vector machine (QSVM), and a hybrid quantum-classical neural network (QNN). In the simulator-based experiment, both SVM and ANN achieved an accuracy of 0.956 with an F1-score of 0.965 on the test set, while QSVM reached an accuracy of 0.807 with an F1-score of 0.866 and the Hybrid QNN achieved 0.623 accuracy with an F1-score of 0.677. These quantum and hybrid models also required substantially longer training times than the classical baselines. A small hardware experiment on an IBM quantum device further illustrates both the practical feasibility and the current limitations of executing this classification task on noisy intermediate-scale quantum hardware.
The rapid advancements in genomics and the availability of large-scale genetic datasets have revolutionized our understanding of genetic diseases. However, the complexity and high dimensionality of genomic data pose significant computational challenges for classical machine learning (ML) algorithms. Quantum machine learning (QML), an emerging interdisciplinary field that combines quantum computing with ML techniques, offers a promising solution to address these challenges. This paper explores the application of QML algorithms for diagnosing genetic diseases by leveraging the unique properties of quantum computing, such as superposition, entanglement, and quantum parallelism. A novel hybrid quantum-classical approach is proposed to enhance the accuracy and efficiency of disease diagnosis using genomic datasets. The methodology involves encoding genetic data into quantum states, applying quantum-enhanced feature selection and classification algorithms, and validating the results on publicly available datasets, such as the UK Biobank and the Cancer Genome Atlas (TCGA). Experimental results demonstrate that the proposed QML framework achieves higher classification accuracy and faster computation times compared to classical counterparts. Equations, tables, and charts are used to illustrate the effectiveness of the approach. This research highlights the transformative potential of QML in precision medicine and lays the groundwork for future investigations into quantum-enabled healthcare solutions.
Classical machine learning has practical significant advancements and extensive acceptance across various domains, enabling the growth of precise predictive models. The purpose of this work is to examine the accuracy gained by various machine learning algorithms, both classical and quantum. In addition, the study delves into the fundamental ideas and principles that form the backbone of quantum machine learning algorithms, such as quantum data representation, quantum feature mapping, and quantum optimization strategies. Experiments use classical machine learning models trained and tested on traditional hardware to measure evaluation metrics like classification accuracy, precision, recall, and F1-score for quantum machine learning models executed on quantum computing platforms or simulators. In quantum SVM, the seed dataset obtained 100% accuracy, and the supermarket dataset obtained 69% accuracy when compared to classical SVM. The findings imply that, under some conditions, quantum machine learning algorithms show the capacity to beat their conventional counterparts in terms of accuracy.
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.
In recent years, quantum machine learning has emerged as a promising intersection between quantum physics and artificial intelligence, particularly in domains requiring advanced pattern recognition such as healthcare. This study investigates the effectiveness of Quantum Support Vector Machines (QSVM), which leverage quantum mechanical phenomena like superposition and entanglement to construct high-dimensional Hilbert spaces for data classification. Focusing on lung cancer diagnosis, a concrete and critical healthcare application, we analyze how different quantum feature maps influence classification performance. Using a real-world dataset of 309 patient records with significant class imbalance (39 non-cancer vs. 270 cancer cases), we constructed six balanced subsets for robust evaluation. QSVM models were implemented using Qiskit and executed on the qasm simulator, employing three distinct quantum feature maps: ZFeatureMap, ZZFeatureMap, and PauliFeatureMap. Performance was assessed using accuracy, precision, recall, specificity, and F1-score. Results show that the PauliFeatureMap consistently outperformed the others, achieving perfect classification in three subsets and strong performance overall. These findings demonstrate how quantum computational principles can be harnessed to enhance diagnostic capabilities, reinforcing the importance of physics-based modeling in emerging AI applications within healthcare.