Quantum & Neuromorphic computing models outperform traditional methods.
27 Jun 2023, 14:24
Quantum & Neuromorphic computing models outperform traditional methods
We ran simulations for the Standard Banknote Authentication dataset and measured the following Key Performing Indicators (KPIs):
Accuracy: the fraction of samples that have been classified correctly
Precision: proportion of correct positive identifications over all positive identifications
Recall: proportion of correct positive identifications over all actual positives
F1 score: harmonic mean of the model's precision and recall
The attached summary shows that Quantum (D-Wave) and Neuromorphic (Dynex) based SVM model training is superior to traditional support vector machines. We used Scikit-learn's LIBSVM using Sequential Miinimal Optimisation as benchmark.