Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers

• URL: http://arxiv.org/abs/2103.03897v1
• Date: Fri, 5 Mar 2021 19:02:31 GMT
• Title: Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers
• Authors: Andrew Blance and Michael Spannowsky
• Abstract summary: Photonic Quantum Computers provides several benefits over the discrete qubit-based paradigm of quantum computing. We build an anomaly detection model to use on searches for New Physics. We 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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