Identification and Mitigating Bias in Quantum Machine Learning

TL;DR

This paper explores the unique biases in quantum machine learning, analyzing their origins, implications, and proposing strategies for identification and mitigation to improve model fairness and reliability.

Contribution

It provides a comprehensive overview of quantum-specific biases, their causes, and introduces methods for detecting and reducing these biases in QML models.

Findings

Quantum biases differ from classical biases in origin and manifestation.

Identification techniques for quantum biases are developed.

Mitigation strategies improve fairness in quantum machine learning models.

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?