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} complexity, with respect to the sample’s feature-vector length, from mathcal{O}(N) to mathcal{O}left(log (N)right) .
Highlights
RejectionLoss (Distance from Centroid)The continuous-variable quantum computing regime differs from traditional, discrete, qubit-based quantum computing in many key areas
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
We propose a novel method using Gaussian boson sampling (GBS) to create the embeddings and perform anomaly detection using a quantum equivalent of K-means known as Q-means
Summary
To use as our background and signal samples we generate pp → Z + jets and pp → HZ events, with subsequent decays H → A1A2, A2 → gg and A1 → gg. We use Pythia 8.2 to generate events and perform parton showering [44] Such a scenario could be realised through derivative interactions between the Higgs boson and the pseudoscalars A1 and A2, which in turn form an effective, yet highly suppressed, interaction with gluons. Their decay to gluons could still be prompt, whereas their direction production cross section in proton collisions was tiny.
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