Alexander Lvovsky

Abstract:

Large arrays of optical parametric oscillators can solve combinatorial optimization problems with potential quantum advantage but are challenging to realize. Gray et al. developed a photonic chip with this capability and elaborated a method to bring these oscillators into controllable interaction, opening new possibilities in quantum and classical optical computing.

Abstract:

A widely tested approach to overcoming the diffraction limit in microscopy without disturbing the sample relies on substituting widefield sample illumination with a structured light beam. This gives rise to confocal, image scanning, and structured illumination microscopy methods. On the other hand, as shown recently by Tsang and others, subdiffractional resolution at the detection end of the microscope can be achieved by replacing the intensity measurement in the image plane with spatial mode demultiplexing. In this work, we study the combined action of Tsang’s method with image scanning. We experimentally demonstrate superior lateral resolution and enhanced image quality compared to either method alone. This result paves the way for integrating spatial demultiplexing into existing microscopes, contributing to further pushing the boundaries of optical resolution.

Abstract:

Optics is an exciting route for the next generation of computing hardware for machine learning, promising several orders of magnitude enhancement in both computational speed and energy efficiency. However, reaching the full capacity of an optical neural network necessitates the computing be implemented optically not only for inference, but also for training. The primary algorithm for network training is backpropagation, in which the calculation is performed in the order opposite to the information flow for inference. While straightforward in a digital computer, optical implementation of backpropagation has remained elusive, particularly because of the conflicting requirements for the optical element that implements the nonlinear activation function. In this work, we address this challenge for the first time with a surprisingly simple scheme, employing saturable absorbers for the role of activation units. Our approach is adaptable to various analog platforms and materials, and demonstrates the possibility of constructing neural networks entirely reliant on analog optical processes for both training and inference tasks.

Abstract:

We exploit polarization self-rotation (PSR) in atomic rubidium vapor to observe spontaneous symmetry breaking and bistability of polarization patterns. We pump the vapor cell with horizontally polarized light while the vertical polarization, which is initially in the vacuum state, is resonated in a ring cavity. Microscopic field fluctuations in this mode experience cumulative gain due to the compound action of amplification due to the self-rotation and feedback through the resonator, eventually acquiring a macroscopic magnitude akin to an optical parametric oscillator. The randomness of these fluctuations results in a bistable, random macroscopic polarization pattern at the output. We propose utilizing this mechanism to simulate an Ising-like interaction between multiple spatial modes and as a basis for a fully optical coherent Ising machine (CIM).