BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:Quantum algorithm for the classification of Supersymmetric top qua rk events DTSTART:20211215T163000Z DTEND:20211215T180000Z DTSTAMP:20260704T035214Z UID:7a74c466-f3a1-48b9-8d29-187d5f7f8465 SEQUENCE:1 CREATED:20211213T143602Z DESCRIPTION: The search for supersymmetric particles is one of the major g oals in the next high luminosity phase of the Large Hadron Collider. Super symmmetric top (stop) searches play a very important role in this respect\ , but the unprecedented collision rate that will be attained at this phase poses new challenges for the separation between any new signal and the St andard Model background. While classical multivariate techniques might be insufficient in this new environment\, the massive parallelism provided by quantum computing techniques may yield an efficient solution for the prob lem. In this paper we make a novel application of the QAML-Z approach to c lassify the stop signal versus the background\, and implement it in a quan tum annealer machine. We show that this approach together with the pre-pro cessing of the data with Principal Component Analysis may yield better res ults than conventional multivariate approaches LAST-MODIFIED:20211213T143602Z LOCATION:Online URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/quantum-algorithm-for-the- classification-of-supersymmetric-top-quark-events/ X-ALT-DESC;FMTTYPE=text/html:

The search for sup ersymmetric particles is one of the major goals in the next high luminosit y phase of the Large Hadron Collider. Supersymmmetric top (stop) searches play a very important role in this respect\, but the unprecedented collisi on rate that will be attained at this phase poses new challenges for the s eparation between any new signal and the Standard Model background. While classical multivariate techniques might be insufficient in this new enviro nment\, the massive parallelism provided by quantum computing techniques m ay yield an efficient solution for the problem. In this paper we make a no vel application of the QAML-Z approach to classify the stop signal versus the background\, and implement it in a quantum annealer machine. We show t hat this approach together with the pre-processing of the data with Princi pal Component Analysis may yield better results than conventional multivar iate approaches

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