IEEE Ottawa Webinar Series on AI and Machine Learning - Sponsored by IEEE Ottawa PHO Chapter, CS Chapter, ComSoc Chapter, TEMS Chapter, and SP Chapter, jointly with Vitesse- Reskilling

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The generation and manipulation of quantum states of light has historically played a critical role in the development of quantum information science: from the first violation of Bell’s inequality to the more recent development of near-term quantum algorithms such as the variational quantum eigensolver. In this talk, I present a new frontier for photons at the intersection of quantum mechanics and machine learning. I will first provide a short introduction to the field of quantum photonics, then demonstrate how quantum photonic processors can accelerate both quantum and classical machine learning. Finally, I show how optimization techniques can enhance large-scale quantum control and provide a new path towards efficient verification of near-term quantum processors.



  Date and Time

  Location

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  • Please note, this is an online webinar
  • All registered attendees will receive an email notice on how to sign in to the webinar
  • Kanata, Ontario
  • Canada
  • Co-sponsored by Vitesse Re-Skilling Canada, IEEE Ottawa PHO Chapter, ComSoc Chapter, CS Chapter, TEMS Chapter, and SP Chapter
  • Starts 08 July 2020 10:40 AM
  • Ends 21 July 2020 11:00 AM
  • All times are America/Montreal
  • No Admission Charge
  • Register


  Speakers

Dr. Jacques Carolan of University of Copenhagen, Danmark

Topic:

Quantum Photonics Processors to Accelerate Machine Learning

The generation and manipulation of quantum states of light has historically played a critical role in the development of quantum information science: from the first violation of Bell’s inequality to the more recent development of near-term quantum algorithms such as the variational quantum eigensolver. In this talk, I present a new frontier for photons at the intersection of quantum mechanics and machine learning. I will first provide a short introduction to the field of quantum photonics, then demonstrate how quantum photonic processors can accelerate both quantum and classical machine learning. Finally, I show how optimization techniques can enhance large-scale quantum control and provide a new path towards efficient verification of near-term quantum processors.

Biography:

Jacques Carolan received his MSci in Physics and Philosophy from the University of Bristol in 2011 where he then joined the Centre for Quantum Photonics to earn a PhD in 2015.  He joined the Quantum Photonics Laboratory at MIT as a Postdoctoral Fellow in 2016 and after receiving a Marie Skłodowska-Curie Global Fellowship, joined the Quantum Photonics Group at the Niels Bohr Institute in 2019.  He was a 2014 EPSRC ICT Pioneer, attended the 66th Lindau Nobel Laureates Meeting in 2016 on behalf of the Royal Society and won an Institute of Physics QEP thesis Commendation and UOB Faculty of Science Commendation for his thesis work.

Address:Copenhagen, Denmark





Agenda

12:00 noon - 12:45 Presentation

12:45-13:00 Q&A