No Arabic abstract
The polarization of light is utilized in many technologies throughout science and engineering. The ability to transform one state of polarization to another is a key enabling technology. Common polarization transformers are simple polarizers and polarization rotators. Simple polarizers change the intensity depending on the input state and can only output a fixed polarized state, while polarization rotators rotates the input Stokes vector in the 3D Stokes space. We demonstrate an all-optical input-agnostic polarization transformer (AI-APT), which transforms all input states of polarization to a particular state that can be polarized or partially polarized. The output state of polarization and intensity depends solely on setup parameters, and not on the input state, thereby the AI-APT functions differently from simple polarizers and polarization rotators. The AI-APT is completely passive, and thus can be used as a polarization controller or stabilizer for single photons and ultrafast pulses. The AI-APT may open a new frontier of partially polarized ultrafast optics.
We review our recent work on tunable, ultrahigh quality factor whispering-gallery-mode bottle microresonators and highlight their applications in nonlinear optics and in quantum optics experiments. Our resonators combine ultra-high quality factors of up to Q = 3.6 times 10^8, a small mode volume, and near-lossless fiber coupling, with a simple and customizable mode structure enabling full tunability. We study, theoretically and experimentally, nonlinear all-optical switching via the Kerr effect when the resonator is operated in an add-drop configuration. This allows us to optically route a single-wavelength cw optical signal between two fiber ports with high efficiency. Finally, we report on progress towards strong coupling of single rubidium atoms to an ultra-high Q mode of an actively stabilized bottle microresonator.
We derive planar permittivity profiles that do not reflect perpendicularly exiting radiation of any frequency. The materials obey the Kramers-Kronig relations and have no regions of gain. Reduction of the Casimir force by means of such materials is also discussed.
We observe a strong polarization dependent optical loss of in-plane light propagation in silicon waveguide due to the presence of graphene. Both transverse-electric (TE) and transverse-magnetic (TM) modes are efficiently (~3 dB) coupled to the graphene on suspended membrane waveguides using an apodized focusing subwavelength grating. The TE mode has 7.7 dB less excess optical loss than the TM mode at 1.5 {mu}m for a 150 {mu}m long waveguide in good agreement with a theoretical model. All-optical modulation of light is demonstrated. There is also a large thermally induced change in waveguide effective index because of optical absorption in graphene.
We developed an all-optical link system for making remote comparisons of two distant ultra-stable optical clocks. An optical carrier transfer system based on a fiber interferometer was employed to compensate the phase noise accumulated during the propagation through a fiber link. Transfer stabilities of $2times10^{-15}$ at 1 second and $4times10^{-18}$ at 1000 seconds were achieved in a 90-km link. An active polarization control system was additionally introduced to maintain the transmitted light in an adequate polarization, and consequently, a stable and reliable comparison was accomplished. The instabilities of the all-optical link system, including those of the erbium doped fiber amplifiers (EDFAs) which are free from phase-noise compensation, were below $2times10^{-15}$ at 1 second and $7times10^{-17}$ at 1000 seconds. The system was available for the direct comparison of two distant $^{87}$Sr lattice clocks via an urban fiber link of 60 km. This technique will be essential for the measuring the reproducibility of optical frequency standards.
Deeplearning algorithms are revolutionising many aspects of modern life. Typically, they are implemented in CMOS-based hardware with severely limited memory access times and inefficient data-routing. All-optical neural networks without any electro-optic