Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries also from noisy and imperfect data and without the knowledge of the order parameter. Here we apply various unsupervised machine learning techniques including anomaly detection and influence functions to experimental data from ultracold atoms. In this way we obtain the topological phase diagram of the Haldane model in a completely unbiased fashion. We show that the methods can successfully be applied to experimental data at finite temperature and to data of Floquet systems, when postprocessing the data to a single micromotion phase. Our work provides a benchmark for unsupervised detection of new exotic phases in complex many-body systems.
Zapraszamy na spotkanie o godzinie 10:15
prof. Michał Matuszewski (Instytut Fizyki PAN)
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 [1]. In 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 [7]. We show why using polaritonics in place of standard nonlinear optical phenomena, is the key to achieving such a performance. [1] M. M. Waldrop, Nature News 530, 144 (2016) [2] G. Wetzstein, A. Ozcan, S. Gigan, S. Fan, D. Englund, M. Soljacic, C. Denz, D. A. Miller, and D. Psaltis, Nature 588 , 39 (2020) [3] B. J. Shastri, A. N. Tait, T. F. de Lima, W. H. Pernice, H. Bhaskaran, C. D. Wright, and P. R. Prucnal, Nature Photonics 15, 102 (2021) [4] A. Opala, S. Ghosh, T. C. Liew, and M. Matuszewski, Physical Review Applied 11 , 064029 (2019) [5] D. Ballarini, A. Gianfrate, R. Panico, A. Opala, S. Ghosh, L. Dominici, V. Ardizzone, M. De Giorgi, G. Lerario, G. Gigli, Timothy C. H. Liew, Michal Matuszewski, and Daniele Sanvitto, Nano Letters 20, 3506 (2020) [6] 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) [7] M. Matuszewski, A. Opala, R. Mirek, M. Furman, M. Król, K. Tyszka, T.C.H. Liew, D. Ballarini, D. Sanvitto, J. Szczytko, B. Piętka, arXiv:2108.12648
Seminarium z użyciem połączenia internetowego https://zoom.us/j/97696726563 (meeting ID: ID 97696726563, password: 314297)Zapraszamy na spotkanie o godzinie 10:15
Hue Thi Nguyen (Instytut Geofizyki WF)
An optical vortex (OV) refers to an optical donut-like beam which exhibits phase singularity surrounded by a helicoidal spatial wave-front and, associated with it, orbital angular momentum. Due to such unique properties OVs have been extensively exploited in the context of their fundamental properties as well as practical applications, particularly, optical tweezing, super-resolution imaging, quantum entanglements, and nano-structured surface machining. These promising applications require efficient approaches to generate reliable, high-quality optical vortices. In addition, from practical points of view, it is desirable to minimize the sizes of generated vortex beams as well as optimize vortex generators. The presentation focuses on flat-surface nano-structured gradient index micro-optical vortex phase mask (nVPM) components and their developments based on nanostructurization technique and effective medium theory. The mask was fabricated utilizing a cost-effective modified stack-and-draw technique. In the first part, I will introduce the concept, design and fabrication of the nanostructured vortex phase masks. Verification of its optical performance in air, water, and ethanol will be also analyzed both numerically and experimentally. Moreover, numerical studies also show that the problem of the undesirable light waveguide effect inside the gradient phase components, which accounts to the non-uniformity of the beam intensity profile, can be mitigated. In addition, possibilities to use nanostructured phase masks to generate vortices with high topological charge will be analyzed. In the next part, I will present the design and fabrication of the fiber-based vortex converter microprobe based on the aforementioned nVPM. The probe comprises a single-mode fiber, coreless fiber spacer, and nGRIN VPM integrated manually using active alignment technique. Both experimental and theoretical verifications will be discussed in this section. The results confirm that the probe efficiently converts fiber guided fundamental ode into an optical OV beam with single topological charge. The research results also confirm that the proposed nanostructurization method for the fabrication of a flat parallel surface nanostructured gradient index element is compatible with the optical fiber technique.
Seminarium z użyciem połączenia internetowego https://zoom.us/j/97696726563 (meeting ID: ID 97696726563, password: 314297)Zapraszamy do sali B2.38, ul. Pasteura 5 o godzinie 10:00
(IFD UW)