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SUMMARY:A practical introduction to quantum computing: from qubits to quan
tum machine learning and beyond
DTSTART;VALUE=DATE-TIME:20201211T093000Z
DTEND;VALUE=DATE-TIME:20201211T113000Z
DTSTAMP;VALUE=DATE-TIME:20210506T050857Z
UID:indico-event-970908@indico.cern.ch
DESCRIPTION:*** The webcast is now over. The YouTube recording and the CDS
recording links are available together with the other links at the botto
m of the Indico page. ***\n\nGeneral description of the course\n\nQuantu
m computing is one the most promising new trends in information processing
. In this course\, we will introduce from scratch the basic concepts of th
e quantum circuit model (qubits\, gates and measures) and use them to stud
y some of the most important quantum algorithms and protocols\, including
those that can be implemented with a few qubits (BB84\, quantum teleportat
ion\, superdense coding...) as well as those that require multi-qubit syst
ems (Deutsch-Jozsa\, Grover\, Shor..). We will also cover some of the most
recent applications of quantum computing in the fields of optimization an
d simulation (with special emphasis on the use of quantum annealing\, the
quantum approximate optimization algorithm and the variational quantum eig
ensolver) and quantum machine learning (for instance\, through the use of
quantum support vector machines and quantum variational classifiers). We w
ill also give examples of how these techniques can be used in chemistry si
mulations and high energy physics problems.\n\nThe focus of the course wil
l be on the practical aspects of quantum computing and on the implementati
on of algorithms in quantum simulators and actual quantum computers (as th
e ones available on the IBM Quantum Experience and D-Wave Leap). No previo
us knowledge of quantum physics is required and\, from the mathematical po
int of view\, only a good command of basic linear algebra is assumed. Some
familiarity with the python programming language would be helpful\, but i
s not required either. \n\n====\n\nLecture 6: Quantum variational algorit
hms and quantum machine learning\n\nVariational Quantum Eigensolver. Intro
duction to Quantum Machine Learning (QSVM\, QGAN\, Quantum Classifiers...)
\n\n===\n\nBiography of the speaker\n\nElías F. Combarro holds degrees f
rom the University of Oviedo (Spain) in both Mathematics (1997\, award for
second highest grades in the country) and Computer Science (2002\, award
for highest grades in the country). After some research stays at the Novos
ibirsk State University (Russia)\, he obtained a Ph.D. in Mathematics (Ovi
edo\, 2001) with a dissertation on the properties of some computable predi
cates under the supervision of Prof. Andrey Morozov. Since 2009\, Elías F
. Combarro has been an associate professor at the Computer Science Departm
ent of the University of Oviedo. He has published more than 50 research pa
pers in international journals on topics such as Computability Theory\, M
achine Learning\, Fuzzy Measures and Computational Algebra. His current re
search focuses on the application Quantum Computing to algebraic\, optimiz
ation and machine learning problems. From July 2020 he has been a Cooperat
ion Associate at CERN openlab.\n\nhttps://indico.cern.ch/event/970908/
LOCATION:
URL:https://indico.cern.ch/event/970908/
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