It has been shown that dimension reduction methods such as Principal Component Analysis (PCA) may be inherently prone to unfairness and treat data from different sensitive groups race, color, sex, etc., unfairly. In pursuit of fairness-enhancing dimensionality reduction, using the notion Pareto optimality, we propose an adaptive first-order algorithm learn a subspace preserves fairness, while s...

Principal component analysis has been widely adopted to reduce the dimension of data while preserving information. The quantum version PCA (qPCA) can be used analyze an unknown low-rank density matrix by rapidly revealing principal components it, i.e. eigenvectors with largest eigenvalues. However, due substantial resource requirement, its experimental implementation remains challenging. Here, ...