Quantum Fair Machine Learning

TL;DR

This paper introduces quantum fair machine learning, exploring how quantum computation affects fairness measures and demonstrating quantum algorithms to satisfy fairness constraints, opening new research directions.

Contribution

It pioneers the field of quantum fair machine learning by analyzing differences from classical methods and demonstrating quantum algorithms for fairness constraints.

Findings

Use of Grover's search for statistical parity

Lower bounds on iterations for fairness

Extension of fairness criteria to quantum setting

Abstract

In this paper, we inaugurate the field of quantum fair machine learning. We undertake a comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms, specifying how the unique features of quantum computation alter measures, metrics and remediation strategies when quantum algorithms are subject to fairness constraints. We present the first results in quantum fair machine learning by demonstrating the use of Grover's search algorithm to satisfy statistical parity constraints imposed on quantum algorithms. We provide lower-bounds on iterations needed to achieve such statistical parity within $\epsilon$-tolerance. We extend canonical Lipschitz-conditioned individual fairness criteria to the quantum setting using quantum metrics. We examine the consequences for typical measures of fairness in machine learning context when quantum information processing and quantum data are involved. Finally, we propose open questions and research programmes for this new field of interest to researchers in computer science, ethics and quantum computation.