Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
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arXiv
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| Format: | Preprint |
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2024
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| author | Herbst, Sabrina De Maio, Vincenzo Brandic, Ivona |
| author_facet | Herbst, Sabrina De Maio, Vincenzo Brandic, Ivona |
| contents | With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which are the cornerstone of urgent analytics in distributed computing. Quantum machine learning could be a solution for the increasing demand of urgent analytics, providing potential theoretical speedups and increased space efficiency. However, challenges such as (1) the encoding of data from the classical to the quantum domain, (2) hyperparameter tuning, and (3) the integration of quantum hardware into a distributed computing continuum limit the adoption of quantum machine learning for urgent analytics. In this work, we investigate the use of Edge computing for the integration of quantum machine learning into a distributed computing continuum, identifying the main challenges and possible solutions. Furthermore, exploring the data encoding and hyperparameter tuning challenges, we present preliminary results for quantum machine learning analytics on an IoT scenario. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case Herbst, Sabrina De Maio, Vincenzo Brandic, Ivona Emerging Technologies Distributed, Parallel, and Cluster Computing Machine Learning With the advent of the Post-Moore era, the scientific community is faced with the challenge of addressing the demands of current data-intensive machine learning applications, which are the cornerstone of urgent analytics in distributed computing. Quantum machine learning could be a solution for the increasing demand of urgent analytics, providing potential theoretical speedups and increased space efficiency. However, challenges such as (1) the encoding of data from the classical to the quantum domain, (2) hyperparameter tuning, and (3) the integration of quantum hardware into a distributed computing continuum limit the adoption of quantum machine learning for urgent analytics. In this work, we investigate the use of Edge computing for the integration of quantum machine learning into a distributed computing continuum, identifying the main challenges and possible solutions. Furthermore, exploring the data encoding and hyperparameter tuning challenges, we present preliminary results for quantum machine learning analytics on an IoT scenario. |
| title | Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case |
| topic | Emerging Technologies Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2402.15542 |