Quantum Inspired Search Algorithm for Bias Mitigation
Harsha Avinash Bhute, Ashish Bhosale, Abhinandan Ashtekar · 2025
Machine Learning (ML) algorithms in real-world applications have created an enormous and significant impact in terms of accuracy, efficiency and reliability. However, maintaining an appropriate bias in these algorithms seems to be a rigorous challenge to ensure fair and precise results obtained from decision making models. Various ML algorithms like Fairness Representation Learning, Adversarial Debiasing and Equalized Odds Post-Processing exist to mitigate bias but due to their computational limitations they lack the ability to do so which can have an adverse impact in sensitive sectors like finance and healthcare. Hence quantum algorithms arise with their promising features that can improve model performance with enhanced accuracy and fairness. In this study, we explore quantum approaches like Grover’s search algorithm and analyse the performance using different metrics. A comparative study of classical machine learning algorithms against quantum algorithms is done on adult income dataset. The results display a significant drop in Statistical Parity Difference (SPD) from 0.5173 (Baseline) to 0.4956 (Quantum) which indicates that the model is now less biased and fair predictions are done by the model. In future, hybrid quantum-machine learning techniques will play a key role in mitigation framework and general ML real-world applications.