# Identification and Mitigating Bias in Quantum Machine Learning

arXiv: https://arxiv.org/abs/2409.19011 · HTML: https://mlsanity.com/paper/2409.19011/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [The Inductive Bias of Quantum Kernels](https://mlsanity.com/paper/2106.03747/) (2021)

## Built on by (most cited first, 1 of 1)

- [Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors](https://mlsanity.com/paper/2604.13951/) (2026)

## Abstract (arXiv)

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?