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VERSION:2.0
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BEGIN:VEVENT
SUMMARY:Variational quantum algorithms for many-body simulation and machin
 e learning problems
DTSTART:20240906T110000Z
DTEND:20240906T130000Z
DTSTAMP:20260622T025208Z
UID:6f887e8e-f35d-4651-8ec8-438c67433338
SEQUENCE:1
CREATED:20240905T132016Z
DESCRIPTION:Near-term quantum simulators suffer from various imperfections
 . A key question is whether such noisy quantum devices can outperform clas
 sical computers. Several demonstrations for quantum advantage have been ac
 hieved for sampling problems in superconducting and optical platforms. Whi
 le these proof of principle experiments show the superiority of quantum co
 mputers\, they do not offer an immediate practical advantage due to the li
 mited practicality of sampling problems. Variational quantum algorithms ar
 e the most promising approach for achieving practical quantum advantage. T
 hese algorithms benefit from a hybrid combination of quantum devices and c
 lassical optimizers. In this seminar\, we show two distinct applications f
 or such algorithms\, namely: (i) quantum simulation of many-body systems\;
  and (ii) machine learning problems. In the former\, we show how symmetrie
 s can be harnessed in optimizing circuit design [1] and be implemented exp
 erimentally in superconducting quantum simulators [2]. For the latter\, a 
 novel error-mitigation algorithm is presented which significantly enhances
  the performance of variational quantum algorithms for supervised machine 
 learning problems [3]. 
LAST-MODIFIED:20240905T132016Z
LOCATION:Room 3.10 - Mathematics Building – 3rd floor
URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/variational-quantum-algori
 thms-for-many-body-simulation-and-machine-learning-problems/
X-ALT-DESC;FMTTYPE=text/html:<p data-block-key="0h7t6">Near-term quantum s
 imulators suffer from various imperfections. A key question is whether suc
 h noisy quantum devices can outperform classical computers. Several demons
 trations for quantum advantage have been achieved for sampling problems in
  superconducting and optical platforms.<br/><br/> While these proof of pri
 nciple experiments show the superiority of quantum computers\, they do not
  offer an immediate practical advantage due to the limited practicality of
  sampling problems. Variational quantum algorithms are the most promising 
 approach for achieving practical quantum advantage. <br/><br/>These algori
 thms benefit from a hybrid combination of quantum devices and classical op
 timizers. In this seminar\, we show two distinct applications for such alg
 orithms\, namely: (i) quantum simulation of many-body systems\; and (ii) m
 achine learning problems.<br/><br/> In the former\, we show how symmetries
  can be harnessed in optimizing circuit design [1] and be implemented expe
 rimentally in superconducting quantum simulators [2]. For the latter\, a n
 ovel error-mitigation algorithm is presented which significantly enhances 
 the performance of variational quantum algorithms for supervised machine l
 earning problems [3]. </p>
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