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.
Related papers
- Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning.
This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
arXiv Detail & Related papers (2024-11-14T12:27:50Z) - Quantum Information Processing with Molecular Nanomagnets: an introduction [49.89725935672549]
We provide an introduction to Quantum Information Processing, focusing on a promising setup for its implementation.
We introduce the basic tools to understand and design quantum algorithms, always referring to their actual realization on a molecular spin architecture.
We present some examples of quantum algorithms proposed and implemented on a molecular spin qudit hardware.
arXiv Detail & Related papers (2024-05-31T16:43:20Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
Quantum generative learning models (QGLMs) may surpass their classical counterparts.
We review the current progress of QGLMs from the perspective of machine learning.
We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
arXiv Detail & Related papers (2022-06-07T07:32:57Z) - Snowmass White Paper: Quantum Computing Systems and Software for
High-energy Physics Research [3.4654477035437328]
We identify challenges and opportunities for developing quantum computing systems and software to advance high-energy physics research.
We describe opportunities for the focused development of algorithms, applications, software, hardware, and infrastructure to support both practical and theoretical applications of quantum computing to HEP problems within the next 10 years.
arXiv Detail & Related papers (2022-03-14T13:23:20Z) - Standard Model Physics and the Digital Quantum Revolution: Thoughts
about the Interface [68.8204255655161]
Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress.
From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
arXiv Detail & Related papers (2021-07-10T06:12:06Z) - Quantum Computing - A new scientific revolution in the making [2.240702708599667]
We advocate the PISQ approach: Perfect Intermediate-Scale Quantum computing based on a well-established concept of perfect qubits.
We expand the quantum road map with (N)FTQC, which stands for (Non) Fault-Tolerant Quantum Computing.
This will allow researchers to focus exclusively on developing new applications by defining the algorithms in terms of perfect qubits.
arXiv Detail & Related papers (2021-06-22T14:56:55Z) - Quantum Computing for Location Determination [6.141741864834815]
We introduce an example for the expected gain of using quantum algorithms for location determination research.
The proposed quantum algorithm has a complexity that is exponentially better than its classical algorithm version, both in space and running time.
We discuss both software and hardware research challenges and opportunities that researchers can build on to explore this exciting new domain.
arXiv Detail & Related papers (2021-06-11T15:39:35Z) - Quantum Computing Methods for Supervised Learning [0.08594140167290096]
Small-scale quantum computers and quantum annealers have been built and are already being sold commercially.
We provide a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems.
arXiv Detail & Related papers (2020-06-22T06:34:42Z) - The prospects of quantum computing in computational molecular biology [0.0]
We examine how current quantum algorithms could revolutionize computational biology and bioinformatics.
There are potential benefits across the entire field, from the ability to process vast amounts of information.
It is also important to recognize the caveats and challenges in this new technology.
arXiv Detail & Related papers (2020-05-26T15:18:05Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.