Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum computing. The availability of quantum processing units (QPUs) to the general scientific community through open API’s (e.g., IBM’s Qiskit) have kindled the interest in developing and testing new approaches to old problems. In this paper, we present a hybrid quantum machine learning framework for health state diagnostics and prognostics. The framework is exemplified using a problem involving ball bearings dataset. To the best of our knowledge, this is the first attempt to harvest and leverage quantum computing to develop and apply a hybrid quantum-classical machine learning approach to a prognostics and health management (PHM) problem. We hope that this paper initiates the exploration and application of quantum machine learning algorithms in areas of risk and reliability.
Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum computing. The availability of quantum processing units (QPUs) to the general scientific community through open API’s (e.g., IBM’s Qiskit) have kindled the interest in developing and testing new approaches to old problems. In this paper, we present a hybrid quantum machine learning framework for health state diagnostics and prognostics. The framework is exemplified using a problem involving ball bearings dataset. To the best of our knowledge, this is the first attempt to harvest and leverage quantum computing to develop and apply a hybrid quantum-classical machine learning approach to a prognostics and health management (PHM) problem. We hope that this paper initiates the exploration and application of quantum machine learning algorithms in areas of risk and reliability.
Quantum computing is a new field that has recently attracted researchers from a broad range of fields due to its representation power, flexibility and promising results in both speed and scalability. Since 2020, laboratories around the globe have started to experiment with models that lie in the juxtaposition between machine learning and quantum computing. The availability of quantum processing units (QPUs) to the general scientific community through open APIs (e.g., Qiskit from IBM) have kindled the interest in developing and testing new approaches to old problems. In this paper, we present a hybrid quantum machine learning framework for health state diagnostics and prognostics. The framework is exemplified using a problem involving ball bearings dataset. To the best of our knowledge, this is the first attempt to harvest and leverage quantum computing to develop and apply a hybrid quantum-classical machine learning approach to a prognostics and health management (PHM) problem. We hope that this paper initiates the exploration and application of quantum machine learning algorithms in areas of risk and reliability.
Academia and enterprises have explored Prognostic and Health Management (PHM) to perform the diagnosis of failure modes via several traditional Machine and Deep Learning methods.However, the computation scenario is heading toward new advances, which include Quantum Processing Units.Due to its promising results in terms of speed and scalability, research centers worldwide began experimenting with models that lay at the intersection of machine learning and quantum computing.In this sense, a new technique that has already been applied in different scenarios is Quantum Machine Learning (QML), which aims to improve conventional methods in terms of performance and results.This work aims to apply QML models for the fault diagnosis of bearings, an important rotating machinery component, by vibration signals.We apply hybrid models involving the encoding and construction of parameterized quantum circuits connected to a classical neural network.The study uses rotation gates and different entanglement gates (CNOT, CZ and iSWAP), and explores the impact of varying the number of the quantum circuits layers.We perform a classical Multilayer Perceptron model for comparisons purposes.We use the database Case Western Reserve University with 10 failure modes.The obtained results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM activities.
Prognostics and Health Management (PHM) concerns predicting machines’ behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines’ state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models’ speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach to diagnose rolling bearings, which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM.
The study conducted by the prognostics and health management (PHM) entity inquires into the prediction of machine behavior to aid in decision-making through failure modes classification via diagnostics and estimation. This is primarily performed as a classification of the state of rotating machines using diversified The methodologies of Machine Learning (ML) and Deep Learning (DL) have garnered significant attention in contemporary research. In recent times, Quantum Computing (QC) has surfaced as a novel domain of inquiry. With applications in various fields such as optimization, artificial intelligence (AI), gaming, cybersecurity, pharmaceuticals, and the energy sector. Despite hardware limitations, QC is considered an approach to accelerate models and enhance computational power. This paper presents a QML approach for detecting defects in rolling element bearings of rotating machinery using vibration signals. The method employs a hybrid model: a classical neural network to represent a class of parameterized quantum circuits (PQCs), specifically the multi-layer perceptron (MLP). Under different configurations of the Variational Quantum Eigensolver (VQE) system, various types of spin gates and unique two-qubit gates such as CNOT, CZ, and ISWAP are implemented in each PQC configuration. The performance of these PQC configurations is evaluated using two databases of varying complexities: JNU with a failure mode of 10, and CWRRU with a failure mode of 12. Performance results show that all QML models outperform the classical MLP for the two databases considered. This indicates the promising potential of QML models in the field of PHM, despite the current fragile quantum environments.
The field of Prognostics and Health Management (PHM) aims to predict the behavior of machines to make informed maintenance decisions. In the Oil and Gas industry, fault mode diagnosis, as a PHM activity, has been applied to rotating machinery such as compressors, centrifugal pumps, and submersible motors using traditional Machine Learning (ML) and Deep Learning techniques. With the emergence of a new and rapidly growing research field called Quantum Computing (QC), there is now potential for even more efficient and accurate predictions. The QC has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations of hardware, QC has been explored to improve the speed and efficiency of ML models. This master thesis focuses on the application of Quantum Machine Learning (QML) to diagnose rolling bearings which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits connected to a classical neural network, the Multi-Layer Perceptron (MLP). The study uses the Variational Quantum Eigensolver framework along with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP), and explores the impact of varying the number of layers (1, 5 and 10) in the quantum circuit. We use two databases of different complexity levels not previously explored with QML, namely Case Western Reserve University (CWRU) and Jiangnan University (JNU), with 10 and 12 failure modes, respectively. For CWRU and JNU, all QML models presented higher accuracy than the classical MLP. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM activities in the Oil and Gas industry.
Prompt diagnosis of disease is a strong driver for decreasing mortality, improving patient care and healthcare expenditures. Classical machine learning (ML), which has shown great potential in bolstering predictive modeling throughout oncology, cardiology, and neurology, runs the risk of excessive data dimensionality along with high computational complexity. Quantum computing is an exciting new paradigm for computation that takes advantage of superposition and entanglement which can be beneficial in learning patterns in high-dimensional spaces and for kernel-based learning. This review provides a systematic summary of the preliminary work on quantum machine learning (QML) with empirical applications to early disease diagnosis, while emphasizing its theoretical foundations, algorithmic developments and application in healthcare. We scope the conversation on quantum support vector machines, variational quantum classifiers, quantum kernels and hybrid architectures for a quantum classical. We also examine current hardware limitations in the NISQ era and assess how close these methods are to being ready for translation into public health applications. Although widespread clinical application is still far away, hybrid QML approaches are finding utility in low-data and high-complexity settings. We conclude with a discussion of research challenges, regulation and strategy towards realizing such quantum-enhanced precision diagnostics through U.S. public health innovation initiatives