Identification and Mitigating Bias in Quantum Machine Learning

• URL: http://arxiv.org/abs/2409.19011v1
• Date: Mon, 23 Sep 2024 21:31:16 GMT
• Title: Identification and Mitigating Bias in Quantum Machine Learning
• Authors: Nandhini Swaminathan, David Danks,
• Abstract summary: 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? What can and should be done about it?
• Score: 1.5442389863546546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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?

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