Speaker
Description
As next-generation colliders reach unprecedented energies and luminosities, novel computing techniques become essential to meet the resulting computational challenges. One area of promise is quantum machine learning (QML), which combines the quantum effects of superposition and entanglement with classical optimisation techniques. Within QML, photonic devices are of particular interest due to their natural compatibility with continuous data and the highly non-trivial inter-qumode correlations they can induce. Here we introduce the 1P1Qm encoding scheme, the continuous-variable analogue of the qubit-based 1P1Q framework, in which classical jet constituents are encoded onto quantum circuits using one particle per qumode. Under this scheme, we study quantum autoencoders for anomaly detection and supervised quantum classifiers for signal/background discrimination, benchmarking both against classical reference models on a $t\bar{t}$ and $Z(\rightarrow \nu\nu)$+jets dataset. In both cases, the quantum models outperform their classical counterparts in the low-data regime and remain competitive at larger training-set sizes, while using substantially fewer trainable parameters.