Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
Standalone vertex finding in the ATLAS muon spectrometer
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abstract
A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bb final states, and pp collision data at sqrt(s) = 7 TeV collected with the ATLAS detector at the LHC during 2011.
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Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.