Fig. 1
From: Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning
Two configurations are shown for a data set X consisting of \(T=2500\) points uniformly distributed on the unit sphere with different levels of noise. (a,c) Scatter plot of the point cloud \(X_A\) for (a) noise = 0, and (c) noise = 0.2, for two corresponding matrix configurations A trained with Hilbert space dimension \(N=3\). The original dataset is overlayed in red. Darker points correspond to lower relative error energy \(E_0\). (b,d) Spectral gaps for (b) noise = 0 and (d) noise = 0.2. The x-axis corresponds to points \(x\in X_A\) and on the y-axis the eigenvalues of the quantum metric g(x) are plotted.
