Identification and Mitigating Bias in Quantum Machine Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910624096714752 |
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| author | Swaminathan, Nandhini Danks, David |
| author_facet | Swaminathan, Nandhini Danks, David |
| contents | As quantum machine learning (QML) emerges as a promising field at the intersection of quantum computing and artificial intelligence, it becomes crucial to address the biases and challenges that arise from the unique nature of quantum systems. This research includes work on identification, diagnosis, and response to biases in Quantum Machine Learning. This paper aims to provide an overview of three key topics: How does bias unique to Quantum Machine Learning look? Why and how can it occur? What can and should be done about it? |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19011 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identification and Mitigating Bias in Quantum Machine Learning Swaminathan, Nandhini Danks, David Quantum Physics Artificial Intelligence Machine Learning As quantum machine learning (QML) emerges as a promising field at the intersection of quantum computing and artificial intelligence, it becomes crucial to address the biases and challenges that arise from the unique nature of quantum systems. This research includes work on identification, diagnosis, and response to biases in Quantum Machine Learning. This paper aims to provide an overview of three key topics: How does bias unique to Quantum Machine Learning look? Why and how can it occur? What can and should be done about it? |
| title | Identification and Mitigating Bias in Quantum Machine Learning |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2409.19011 |