QWebinars

Welcome to QWorld’s webinar series ­čÖé

In our webinars, we focus on scientific and popular topics and inspiring stories mostly from the quantum world and sometimes from the world.

Contact: qwebinar [at] qworld.lu.lv


Our fifth QWebinar

Q&A session with Scott Aaronson on quantum computing from the 80s to 20s
18:00 (CEST), July 15, 2020

Moderators: Andris Ambainis (QLatvia) and Zoltán Zimborás (QHungary)
Organizers: Abuzer Yakary─▒lmaz (QLatvia) and Agnieszka Wolska (QLatvia)

See the recording of the event on YouTube >>

About Scott:

Scott Aaronson is David J. Bruton Centennial Professor of Computer Science at the University of Texas at Austin. He received his bachelor’s from Cornell University and his PhD from UC Berkeley. Before coming to UT Austin, he spent nine years as a professor in Electrical Engineering and Computer Science at MIT. Aaronson’s research in theoretical computer science has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science FoundationÔÇÖs Alan T. Waterman Award, the United States PECASE Award, and the Tomassoni-Chisesi Prize in Physics.



Our last QWebinar

Quantum Machine Learning and PennyLane | 18:00 (CEST), June 17, 2020
by Maria Schuld (Xanadu and University of KwaZulu-Natal)

Moderators: Abuzer Yakary─▒lmaz (QLatvia) and Ay┼čin Ta┼čdelen (QTurkey)
Organizers: Abuzer Yakary─▒lmaz (QLatvia) and Agnieszka Wolska (QLatvia)

See the recording of the event on YouTube >>

From Maria:

Algorithms that run on quantum computers – so-called quantum circuits – underlie different laws of information processing than conventional computations. By optimizing the physical parameters of quantum circuits we can use them like neural networks, and train circuits to generalize from data. This talk highlights different aspects of such “variational quantum machine learning algorithms”, including their role in the development of near-term quantum technologies, their interpretation as a cross-breed of neural networks and support vector machines, and strategies of fitting the quantum model to data. As a practical implementation, I will show how to use the open-source software framework “PennyLane” to integrate quantum circuits with machine learning libraries such as PyTorch and Tensorflow.

About Maria:

Maria Schuld works as a researcher for the Toronto-based quantum computing start-up Xanadu, as well as for the Big Data and Informatics Flagship of the University of KwaZulu-Natal in Durban, South Africa.
She received her PhD from the University of KwaZulu-Natal in 2017 for her work on the intersection of quantum computing and machine learning, which was published as the book “Supervised Learning with Quantum Computers” (Springer, 2018, co-authored by F. Petruccione). Besides her physics background Maria has a postgraduate degree in political science, and a keen interest in the interplay of emerging technologies and society.




Previous QWebinars


June 03, 2020 | Ronald de Wolf
The Potential Impact of Quantum Computers on Society

Check the details and recording of the webinar >>


May 13, 2020 | Nathan Shammah
My personal quantum software story: QuTiP and Unitary Fund
Check the details and recording of the webinar >>


April 29, 2020 | Paweł Gora
Introduction to Quantum Computing

Check the details and recording of the webinar >>