We have 3 of 3 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This work adds the missing non-linearity to UCI Default of Credit Card Clients data with a quantum-inspired feature map, specific to linear models: Random Forest, SVM, XGBoost, and k-NN, already non-linear, do not benefit.
It is shown that how the 8 input features are chosen matters: Random Forest importance-guided selection reaches F1 = 0.523, while encoding maximally uncorrelated features drops it to 0.496, demonstrating that the circuit amplifies informative structure rather than creating it from scratch.
Comparing parameterized quantum circuits integrated as residual sidecar modules within a ResNet-50 backbone for 31-class neural population decoding---imagined handwriting classification from multi-neuron spike rasters finds measurement-guided training consistently improves representation geometry without reducing accur...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
