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PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20260217T154856Z
UID:DADBFF8B-225E-4C35-9352-B2901A966048
DTSTART;TZID=Asia/Kolkata:20260214T190000
DTEND;TZID=Asia/Kolkata:20260214T200000
DESCRIPTION:The lecture series is planned for the benefit of the research s
 cholars\, PG scholars\, faculty of various academic institutions and Indus
 try professionals across the world.\n\nQuantum computing lecture series of
 fer foundational and advanced insights into quantum mechanics\, algorithms
 \, and real-world applications using platforms like Qiskit and IBM Quantum
 .\n\nThe fourth part is related to Quantum Machine Learning\n\nQuantum Mac
 hine Learning (QML) combines principles of quantum computing with machine 
 learning to address computational challenges beyond the capabilities of cl
 assical systems. By exploiting quantum phenomena such as superposition and
  entanglement\, QML aims to improve learning efficiency\, optimization\, a
 nd data analysis for complex problems. This lecture introduces the fundame
 ntals of Quantum Machine Learning\, including quantum data encoding\, hybr
 id quantum–classical algorithms\, and variational models. It also discus
 ses the opportunities and limitations of implementing QML on current noisy
  intermediate-scale quantum (NISQ) devices. The session provides a concise
  overview of ongoing research\, practical applications\, and future prospe
 cts of QML.\n\nSpeaker(s): Guncha Malik\, Sathish Krishna\n\nAgenda: \n- W
 elcome address by ATPP Subsection Chair\n- Introduction of speakers\n- Ses
 sion by speakers\n- Concluding Remarks\n- Virtual Memento distribution\n- 
 Vote of thanks\n\nVirtual: https://events.vtools.ieee.org/m/538169
LOCATION:Virtual: https://events.vtools.ieee.org/m/538169
ORGANIZER:c.h.r@ieee.org
SEQUENCE:19
SUMMARY:IEEE ATPSS-R0014903-Lecture Series on Quantum Computing-Part-4 Quan
 tum Machine Learning
URL;VALUE=URI:https://events.vtools.ieee.org/m/538169
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;The lecture series is planned for the bene
 fit of the research scholars\, PG scholars\, faculty of various academic i
 nstitutions and Industry professionals across the world.&lt;/p&gt;\n&lt;p&gt;&lt;!--Start
 Fragment --&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Quantum computing lecture series offer founda
 tional and advanced insights into quantum mechanics\, algorithms\, and rea
 l-world applications using platforms like Qiskit and IBM Quantum.&lt;/strong&gt;
 &lt;/p&gt;\n&lt;p&gt;&lt;!--EndFragment --&gt;&lt;/p&gt;\n&lt;p&gt;The fourth part is related to Quantum
  Machine Learning&lt;/p&gt;\n&lt;p&gt;Quantum Machine Learning (QML) combines principl
 es of quantum computing with machine learning to address computational cha
 llenges beyond the capabilities of classical systems. By exploiting quantu
 m phenomena such as superposition and entanglement\, QML aims to improve l
 earning efficiency\, optimization\, and data analysis for complex problems
 . This lecture introduces the fundamentals of Quantum Machine Learning\, i
 ncluding quantum data encoding\, hybrid quantum&amp;ndash\;classical algorithm
 s\, and variational models. It also discusses the opportunities and limita
 tions of implementing QML on current noisy intermediate-scale quantum (NIS
 Q) devices. The session provides a concise overview of ongoing research\, 
 practical applications\, and future prospects of QML.&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agend
 a: &lt;br /&gt;&lt;ol&gt;\n&lt;li&gt;Welcome address by ATPP Subsection Chair&lt;/li&gt;\n&lt;li&gt;Intr
 oduction of speakers&lt;/li&gt;\n&lt;li&gt;Session by speakers&lt;/li&gt;\n&lt;li&gt;Concluding Re
 marks&lt;/li&gt;\n&lt;li&gt;Virtual Memento distribution&lt;/li&gt;\n&lt;li&gt;Vote of thanks&lt;/li&gt;
 \n&lt;/ol&gt;
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