Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908593254563840 |
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| author | Flöther, Frederik F. Mikolon, Jan Longobardi, Maria |
| author_facet | Flöther, Frederik F. Mikolon, Jan Longobardi, Maria |
| contents | Research and usage of artificial intelligence, particularly generative and large language models, have rapidly progressed over the last years. This has, however, given rise to issues due to high energy consumption. While quantum computing is not (yet) mainstream, its intersection with machine learning is especially promising, and the technology could alleviate some of these energy challenges. In this perspective article, we break down the lifecycle stages of large language models and discuss relevant enhancements based on quantum algorithms that may aid energy efficiency and sustainability, including industry application examples and open research problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20720 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms Flöther, Frederik F. Mikolon, Jan Longobardi, Maria Quantum Physics Research and usage of artificial intelligence, particularly generative and large language models, have rapidly progressed over the last years. This has, however, given rise to issues due to high energy consumption. While quantum computing is not (yet) mainstream, its intersection with machine learning is especially promising, and the technology could alleviate some of these energy challenges. In this perspective article, we break down the lifecycle stages of large language models and discuss relevant enhancements based on quantum algorithms that may aid energy efficiency and sustainability, including industry application examples and open research problems. |
| title | Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2508.20720 |