On Monday, 4 APRIL, at 4.30 p.m., Jerzy Pniewski and Leopold Infeld Colloquium of the Faculty of Physics of the University of Warsaw will take place. The meeting will be exclusively ONLINE (registration required).
Our guest will be:
prof. Michał Matuszewski,
Instytut of Physics, Polish Academy of Science, Warsaw
who will give a lecture: "Efficient optical computing with exciton-polaritons".
There are more and more suggestions that the computation of the future will utilize photons instead of, currently used, electrons. Moreover, modern directions in information processing such, as machine learning based on physical systems require strong non-linearities. Exciton-polaritons being a superposition of the photon and excitation of the solid-state crystal are a very promising platform in that view. The talk will highlight recent advances in photonics-based computation, in particular involving exciton-polaritons.
- Those who registered for the Seminar on 13.12.2021 or later one do not have to register again.
These attendees will receive a reminder on the day of the Seminar with a link allowing them to participate in the lecture.
- Those who did not attend the previous Colloquium are asked to register for the meeting by clicking on the link: https://us02web.zoom.us/meeting/register/tZIpcOuurDwoHtKL-lYSDQP7e_bd31iTMbhx
After registering you will receive an e-mail confirmation with information on how to join the meeting.
- We strongly recommend providing professional email addresses, not private ones, when registering.
- We encourage you not to leave registration to the last minute. Those who register late will not be guaranteed to join the meeting on time.
For informal discussions, please join us starting at 4 pm.
With best regards,
prof. Michał Matuszewski,
Instytut Fizyki Polskiej Akademii Nauk, Warszawa
Efficient optical computing with exciton-polaritons
Recent years have witnessed remarkable developments in big data, artificial intelligence and neural networks. Machine learning has found wide applications in both research and the industry. This comes at the cost of high levels of energy consumption that are necessary to process large amounts of data. It is expected that over 20% of global electricity use by 2030 will be used for information processing. The performance of complementary metal-oxide semiconductors (CMOS) no longer follows Moore's law . As result, much research has been aimed at finding an alternative platform for information processing, characterized by high performance and energy efficiency.
In this talk, I will review recent progress in machine learning with photons [2,3]. Photonic information processing benefits from high speed, parallelization, low communication losses, and high bandwidth. Fully functional photonic neurons, including spiking neurons, as well as neural networks, have been already realized in laboratories. Several networks achieved high performance in challenging machine learning tasks, such as image and video recognition.
We recently demonstrated hardware neural network systems where strong optical nonlinearity results solely from interactions of exciton-polaritons, quantum superpositions of light and matter [4,5,6]. Such superpositions, in the form of mixed quasiparticles of photons and excitons, are characterized by excellent photon-mediated transport properties and strong exciton-mediated interactions. These semiconductor microcavity systems can be used to construct fully all-optical neural networks characterized by extremely high energy efficiency . We show why using polaritonics in place of standard nonlinear optical phenomena, is the key to achieving such a performance.
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 R. Mirek, A. Opala, P. Comaron, M. Furman, M. Król, K. Tyszka, B. Seredynski, D. Ballarini, D. Sanvitto, Timothy C. H. Liew, Wojciech Pacuski, Jan Suffczyński, Jacek Szczytko, Michał Matuszewski, and Barbara Piętka, Nano Letters (2021)
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