Optimizing Hyperparameters for Quantum Data Re-Uploaders in Calorimetric Particle Identification
This work addresses particle identification in physics experiments with a quantum machine learning approach, but it is incremental as it applies an existing method to new data.
The paper tackled particle classification in calorimetric experiments using a single-qubit Data Re-Uploading quantum model optimized for NISQ devices, achieving high accuracy on a novel simulated dataset with efficient computational costs.
We present an application of a single-qubit Data Re-Uploading (QRU) quantum model for particle classification in calorimetric experiments. Optimized for Noisy Intermediate-Scale Quantum (NISQ) devices, this model requires minimal qubits while delivering strong classification performance. Evaluated on a novel simulated dataset specific to particle physics, the QRU model achieves high accuracy in classifying particle types. Through a systematic exploration of model hyperparameters -- such as circuit depth, rotation gates, input normalization and the number of trainable parameters per input -- and training parameters like batch size, optimizer, loss function and learning rate, we assess their individual impacts on model accuracy and efficiency. Additionally, we apply global optimization methods, uncovering hyperparameter correlations that further enhance performance. Our results indicate that the QRU model attains significant accuracy with efficient computational costs, underscoring its potential for practical quantum machine learning applications.