REVIEW 4 cited by
Quantum anomaly detection in the latent space of proton collision events at the LHC
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The ongoing quest to discover new phenomena at the LHC necessitates the continuous development of algorithms and technologies. Established approaches like machine learning, along with emerging technologies such as quantum computing show promise in the enhancement of experimental capabilities. In this work, we propose a strategy for anomaly detection tasks at the LHC based on unsupervised quantum machine learning, and demonstrate its effectiveness in identifying new phenomena. The designed quantum models, an unsupervised kernel machine and two clustering algorithms, are trained to detect new-physics events using a latent representation of LHC data, generated by an autoencoder designed to accommodate current quantum hardware limitations on problem size. For kernel-based anomaly detection, we implement an instance of the model on a quantum computer, and we identify a regime where it significantly outperforms its classical counterparts. We show that the observed performance enhancement is related to the quantum resources utilised by the model.
Forward citations
Cited by 4 Pith papers
-
Unlocking Multidimensional Integration with Quantum Adaptive Importance Sampling
QAIS uses a parameterized quantum circuit to allocate Monte Carlo samples along a learned non-separable density and achieves VEGAS-competitive or better accuracy on correlated integrands in simulation.
-
Quantum similarity learning for anomaly detection
A hybrid Transformer-quantum circuit similarity-learning network reaches AUC 96.1% on simulated di-Higgs anomaly detection, slightly above a classical baseline, with clustering mitigating shot noise.
-
Unsupervised Quantum Anomaly Detection on Noisy Quantum Processors
On a synthetic financial dataset, one-class SVMs using projected quantum kernels achieved higher mean F1 scores than a classical rbf-kernel baseline at every tested anomaly ratio, both in simulation and on quantum har...
-
Overview of Applications of Quantum Computing in QCD
A concise literature overview of quantum algorithms for QCD and collider tasks, stressing possible advantages over classical methods and NISQ hardware limits.
Discussion (0). Continue with ORCID to comment.