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PRODID:UW-Madison-Physics-Events
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SEQUENCE:0
UID:UW-Physics-Event-6185
DTSTART:20201104T160000Z
DTEND:20201104T170000Z
DTSTAMP:20260414T234156Z
LAST-MODIFIED:20201103T204814Z
LOCATION:Livestreaming on QuantHEP Seminar YouTube channel: https://ww
 w.youtube.com/channel/UC44_wEmSKR55S2RM
SUMMARY:APPLICATION OF QUANTUM MACHINE LEARNING TO HIGH ENERGY PHYSICS
  ANALYSIS AT LHC USING QUANTUM COMPUTER SIMULATORS AND QUANTUM COMPUTE
 R HARDWARE\, Wisconsin Quantum Institute\, Sau Lan Wu\, UW–Madison P
 hysics\, CERN
DESCRIPTION:Machine learning enjoys widespread success in High Energy 
 Physics (HEP) analysis at LHC. However the ambitious HL-LHC program wi
 ll require much more computing resources in the next two decades. Quan
 tum computing may offer speed-up for HEP physics analysis at HL-LHC\, 
 and can be a new computational paradigm for big data analysis in High 
 Energy Physics.<br>
\n<br>
\nWe have successfully employed Variational
  Quantum Classifier (VQC) method\, Quantum Support Vector Machine Kern
 el (QSVM-kernel) method and Quantum Neural Network (QNN) method for tw
 o LHC flagship analyses: ttH (Higgs production in association with two
  top quarks) and H->mumu (Higgs decay to two muons\, the second genera
 tion fermions).<br>
\n<br>
\nWe will present our experiences and resul
 ts of a study on LHC High Energy Physics data analysis with IBM Quantu
 m Simulator and Quantum Hardware (using IBM Qiskit framework)\, Google
  Quantum Simulator (using Google Cirq framework)\, and Amazon Quantum 
 Simulator (using Amazon Braket cloud service). The work is in the cont
 ext of a Qubit platform. Taking into account the present limitation of
  hardware access\, different quantum machine learning methods are stud
 ied on simulators and the results are compared with classical machine 
 learning methods (BDT\, classical Support Vector Machine and classical
  Neural Network). Furthermore\, we do apply quantum machine learning o
 n IBM quantum hardware to compare performance between quantum simulato
 r and quantum hardware.<br>
\n<br>
\nThe work is performed by an inter
 national and interdisciplinary collaboration with the Department of Ph
 ysics and Department of Computer Sciences of University of Wisconsin\,
  CERN Quantum Technology Initiative\, IBM Research Zurich\, Fermilab Q
 uantum Institute\, BNL Computational Science Initiative\, State Univer
 sity of New York at Stony Brook\, and Quantum Computing and AI Researc
 h of Amazon Web Services.<br>
\n<br>
\nThis work pioneers a close coll
 aboration of academic institutions with industrial corporations in a H
 igh Energy Physics analysis effort.<br>
\n<br>
\nAlthough the era of e
 fficient quantum computing may still be years away\, we have made prom
 ising progress and obtained preliminary results in applying quantum ma
 chine learning to High Energy Physics. A PROOF OF PRINCIPLE.
URL:https://www.physics.wisc.edu/events/?id=6185
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