Speaker
Description
In collider physics, jet tagging is a key classification task in which models must identify the initiating particle from a reconstructed jet's internal structure. Though their final predictions are often produced by classical neural-network heads, Particle Transformer architectures achieve strong performance by learning interactions among jet constituents. In this paper, the usefulness of a variational quantum circuit as an alternate decision layer for Particle Transformer representations is investigated.
A pretrained Particle Transformer serves as a frozen feature extractor using a controlled subset of public JetClass dataset. The resulting jet representations can be encoded into simulated four-to eight-qubit variational quantum circuits by compressing them into low-dimensional vectors. Linear, parameter-matched, and deeper multilayer-perceptron classifiers trained on the identical compressed inputs are compared to the quantum heads. Additionally, a classical classifier using the original, uncompressed transformer representation is included, allowing the performance of the quantum model itself to be distinguished from the information loss resulting from compression.
Binary and selected multiclass jet-tagging are taken into consideration in this work. Classification accuracy, ROC-AUC, background rejection, calibration, stability across random initializations, trainable parameter count, and simulator-based computing cost are used to evaluate performance. The bottleneck dimension, circuit depth, entanglement pattern, and training-set size are varied in further tests. The goal is to provide a assessment of where variational quantum classification heads may be competitive and where current limitations still exist, rather than assuming that the quantum model will outperform classical alternatives.