Warsaw Quantum Computing Group

We invite you to attend (online) Episode LIV of Warsaw Quantum Computing Group meetups!

"Machine learning and optical quantum information"

Karol Bartkiewicz

7.12.2023, 18:00 UTC+1  

Abstract:

Classical programming means writing explicit instructions so that a program processes the input data and correctly answers our questions. Machine learning (ML) is a branch of artificial intelligence research that uses implicit programming, where the program does not receive explicit instructions. This method is particularly suitable for problems that are intuitive to humans but difficult to convert to a set of machine instructions. Some complex problems resist known ML methods, especially in quantum systems [1,3]. E.g. designing new drug molecules or supervising quantum communication networks, which under certain assumptions should be protected from eavesdropping by the laws of quantum physics. These tasks quickly become unfeasible as the complexity of the problem increases. Solutions to such problems must be sought using quantum computing for ML [1,2]. This is the original motivation to combine ML and quantum physics [1]. However, there are many other reasons to do so. In particular, ML can be used to motivate theoretical and experimental research in quantum information, quantum state engineering, classification, and detection. To illustrate this, I will discuss a few assorted examples of combining ML and quantum information processing, including [2,4,5,6].

 

Keywords:

optical quantum information, quantum machine learning, kernel trick, classification, clustering, Hilbert-Schmidt distance, variational quantum circuits, entanglement detection

 

References:

[1] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, S. Lloyd, Nature 549, 195 (2017).

[2] V. Trávníček, K. Bartkiewicz, A. Černoch, K. Lemr, Phys. Rev. Lett. 123, 260501 (2019).

[3] G. Carleo et al., Rev. Mod. Phys. 91, 045002 (2019).

[4] K. Bartkiewicz, C. Gneiting, A. Černoch, K. Jiráková, K. Lemr, F. Nori, Sci. Rep. 10, 12356 (2020).

[5] J. Jašek, K. Jiráková, K. Bartkiewicz, A. Černoch, T. Fürst, K. Lemr, Opt. Express 27, 32454 (2019).

[6] K. Bartkiewicz, P. Tulewicz, J. Roik, K. Lemr, Sci. Rep. 13, 12893 (2023).


BIO:
Karol Bartkiewicz

Institute of Spintronics and Quantum Information, Faculty of Physics, Adam Mickiewicz University in Poznań, Uniwersytetu Poznańskiego 2, 61-614 Poznań, Poland

 

K. Bartkiewicz earned PhD in physics in 2012 at Adam Mickiewicz University in Poznań (AMU), Poland. As of 2019, he is an associate professor at the Faculty of Physics at AMU. For ten years he has been working as a researcher (applied physics) at Palacký University in the Czech Republic. He has co-authored more than 50 publications on quantum optics, quantum information processing and quantum information, two of which on secure quantum communication protocols have been commented on in specialized (e.g. Nature Physics) and popular media (e.g. New Scientist, Science Daily, TVN 24). For the past six years, he has been working on quantum machine learning as one of the most promising applications of quantum computing.

The meeting is organized by the Quantum AI Foundation.

Strategic Partners: Snarto, Cogit, Sonovero R&D