Modified Layerwise Learning for Data Re-uploading Classifier in High-Energy Physics Event Classification

This paper aims to demonstrate the use of modified layerwise learning on a data-reuploading classifier, where the parameterized quantum circuit will be used as a quantum classifier to classify the SUSY dataset. We managed to produce a better result using this approach compared to the previous related research with fewer qubits. We obtained an AUC of 0.849 on a testing dataset with 5000 training and testing samples, trained and tested using a state-vector simulator. We also tested to run the circuit on Rigetti’s Aspen-9 quantum processing unit provided by AWS using the already optimized parameter to predict 2000 samples of the test dataset and obtained an AUC of 0.830.