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Found 5 papers, 2 papers with code
Guided Quantum Compression for Higgs Identification
To ameliorate this issue, we design an architecture that unifies the preprocessing and quantum classification algorithms into a single trainable model: the guided quantum compression model.
Machine Learning for Anomaly Detection in Particle Physics
The detection of out-of-distribution data points is a common task in particle physics.
Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm
We expand upon this result and identify a speedup utilizing Grover's algorithm in the kernel of a support vector machine.
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC).
Quantum anomaly detection in the latent space of proton collision events at the LHC
The designed quantum anomaly detection models, namely an unsupervised kernel machine and two clustering algorithms, are trained to find new-physics events in the latent representation of LHC data produced by the autoencoder.
