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Register a new userOn the use of deep neural networks in optical communications
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Information transfer rates in optical communications may be dramatically increased by making use of spatially non-Gaussian states of light. Here we demonstrate the ability of deep neural networks to classify numerically-generated, noisy Laguerre-Gauss modes of up to 100 quanta of orbital angular momentum with near-unity fidelity. The scheme relies only on the intensity profile of the detected modes, allowing for considerable simplification of current measurement schemes required to sort the states containing increasing degrees of orbital angular momentum. We also present results that show the strength of deep neural networks in the classification of experimental superpositions of Laguerre-Gauss modes when the networks are trained solely using simulated images. It is anticipated that these results will allow for an enhancement of current optical communications technologies.
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Modern fiber-optic coherent communications employ advanced spectrally-efficient modulation formats that require sophisticated narrow linewidth local oscillators (LOs) and complex digital signal processing (DSP). Here, we establish a novel approach to carrier recovery harnessing large-gain stimulated Brillouin scattering (SBS) on a photonic chip for up to 116.82 Gbit/sec self-coherent optical signals, eliminating the need for a separate LO. In contrast to SBS processing on-fiber, our solution provides phase and polarization stability while the narrow SBS linewidth allows for a record-breaking small guardband of ~265 MHz, resulting in higher spectral-efficiency than benchmark self-coherent schemes. This approach reveals comparable performance to state-of-the-art coherent optical receivers without requiring advanced DSP. Our demonstration develops a low-noise and frequency-preserving filter that synchronously regenerates a low-power narrowband optical tone that could relax the requirements on very-high-order modulation signaling and be useful in long-baseline interferometry for precision optical timing or reconstructing a reference tone for quantum-state measurements.
An optical neural network (ONN) is a promising system due to its high-speed and low-power operation. Its linear unit performs a multiplication of an input vector and a weight matrix in optical analog circuits. Among them, a circuit with a multiple-layered structure of programmable Mach-Zehnder interferometers (MZIs) can realize a specific class of unitary matrices with a limited number of MZIs as its weight matrix. The circuit is effective for balancing the number of programmable MZIs and ONN performance. However, it takes a lot of time to learn MZI parameters of the circuit with a conventional automatic differentiation (AD), which machine learning platforms are equipped with. To solve the time-consuming problem, we propose an acceleration method for learning MZI parameters. We create customized complex-valued derivatives for an MZI, exploiting Wirtinger derivatives and a chain rule. They are incorporated into our newly developed function module implemented in C++ to collectively calculate their values in a multi-layered structure. Our method is simple, fast, and versatile as well as compatible with the conventional AD. We demonstrate that our method works 20 times faster than the conventional AD when a pixel-by-pixel MNIST task is performed in a complex-valued recurrent neural network with an MZI-based hidden unit.
This tutorial reviews the Holevo capacity limit as a universal tool to analyze the ultimate transmission rates in a variety of optical communication scenarios, ranging from conventional optically amplified fiber links to free-space communication with power-limited optical signals. The canonical additive white Gaussian noise model is used to describe the propagation of the optical signal. The Holevo limit exceeds substantially the standard Shannon limit when the power spectral density of noise acquired in the course of propagation is small compared to the energy of a single photon at the carrier frequency per unit time-bandwidth area. General results are illustrated with a discussion of efficient communication strategies in the photon-starved regime.
Surface acoustic wave (SAW) is utilized in diverse fields ranging from physics, engineering, to biology, for transducing, sensing and processing various signals. Optical imaging of SAW provides valuable information since the amplitude and the phase of the displacement field can be measured locally with the resolution limited by the spot size of the optical beam. So far, optical imaging techniques rely on modulation of optical path, phase, or diffraction associated with SAW. Here, we report experiments showing that SAW can be imaged with an optical polarimetry. Since the amount of polarization rotation can be straightforwardly calibrated when polarimeters work in the shot-noise-limited regime, the polarimetric imaging of SAW is beneficial for quantitative studies of SAW-based technologies.
We report an optical homogeneous linewidth of 580 $pm$ 20 Hz of Er$^{3+}$:Y$_2$O$_3$ ceramics at millikelvin temperatures, narrowest so far in rare-earth doped ceramics. We show slow spectral diffusion of $sim$2 kHz over a millisecond time scale. Temperature, field dependence of optical coherence and spectral diffusions reveal the remaining dephasing mechanism as elastic two-level systems in polycrystalline grain boundaries and superhyperfine interactions of Er$^{3+}$ with nuclear spins. In addition, we perform spectral holeburning and measure up to 5 s hole lifetimes. These spectroscopic results put Er$^{3+}$:Y$_2$O$_3$ ceramics as a promising candidate for telecommunication quantum memories and light-matter interfaces.
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