Quantum Machine Learning for Health State Diagnosis and Prognostics
- URL: http://arxiv.org/abs/2108.12265v1
- Date: Wed, 25 Aug 2021 22:57:14 GMT
- Title: Quantum Machine Learning for Health State Diagnosis and Prognostics
- Authors: Gabriel San Mart\'in, Enrique L\'opez Droguett
- Abstract summary: We present a hybrid quantum machine learning framework for health state diagnostics and prognostics.
We hope that this paper initiates the exploration and application of quantum machine learning algorithms in areas of risk and reliability.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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