Abstract
Abstract
Photonic Quantum Computers provide 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 $$ \mathcal{O} $$
O
complexity, with respect to the sample’s feature-vector length, from $$ \mathcal{O}(N) $$
O
N
to $$ \mathcal{O}\left(\log (N)\right) $$
O
log
N
.
Publisher
Springer Science and Business Media LLC
Subject
Nuclear and High Energy Physics
Cited by
37 articles.
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