  Photonic Quantum Computers provides several benefits over the discrete
qubit-based paradigm of quantum computing. By using the power of
continuous-variable computing we build an anomaly detection model to use on
searches for New Physics. Our model uses Gaussian Boson Sampling, a $\#$P-hard
problem and thus not efficiently accessible to classical devices. This is used
to create feature vectors from graph data, a natural format for representing
data of high-energy collision events. A simple K-means clustering algorithm is
used to provide a baseline method of classification. We then present a novel
method of anomaly detection, combining the use of Gaussian Boson Sampling and a
quantum extension to K-means known as Q-means. This is found to give equivalent
results compared to the classical clustering version while also reducing the
$\mathcal{O}$ complexity, with respect to the sample's feature-vector length,
from $\mathcal{O}(N)$ to $\mathcal{O}(\mbox{log}(N))$. Due to the speed of the
sampling algorithm and the feasibility of near-term photonic quantum devices,
anomaly detection at the trigger level can become practical in future LHC runs.
