A Parameter-Matched Comparison of Quantum and Classical Neural Networks for Classifying Simulated Supersymmetric Events

Supersymmetry (SUSY) is a proposed hypothetical extension of the Standard Model (SM) that relates bosons and fermions differing in spin by one-half. Certain SUSY models may help address the hierarchy problem, provide a dark-matter candidate, and achieve gauge-coupling unification. Quantum machine learning (QML) has been investigated as a possible tool for high-energy physics (HEP) event classification, including the discrimination of SUSY signal from SM background. Rather than assessing the general feasibility of variational quantum classifiers, this study performs a strict, parameter-matched comparison between a classical Artificial Neural Network (ANN) and a classically simulated Quantum Neural Network (QNN), also considering training time as a point of comparison. Both models were trained on the same 18 input features from a 5000-event subset of the SUSY benchmark dataset, split 70/15/15 into stratified training, validation and test sets. The compact classical ANN (18→3→1 architecture) contained 61 trainable parameters; the QNN---amplitude embedding on five qubits, six variational blocks of RY - RX rotations, and a trainable classical output layer---contained 66 trainable parameters. Each model was optimised using the Adam optimiser with a learning rate of 1*10^{-3} and binary cross-entropy loss for 200 epochs, repeated across 100 independent training runs, each containing a different split sample. Mean test metrics were highly comparable: the QNN achieved marginally higher accuracy (79.0% vs 78.9%), recall and F1 score, while the ANN achieved higher precision, higher AUC (0.858 vs. 0.857) and lower loss (0.455 vs. 0.459), with no metric differing by more than 0.8 percentage points. However, the ANN completed its 100 training runs in 20 minutes, versus around 5 hours for the classically simulated QNN, taking almost 15 times as long to perform the same task. These results do not imply a quantum advantage, but confirm that a compact variational QNN can learn to distinguish SUSY signal from SM background at a level comparable to that of a classical model, but at a computational cost that makes it far less efficient.