Data protection and confidentiality have become a serious concern in today’s world. Their security is guaranteed by cryptographic protocols, which heavily rely on random numbers as a measure against predictability. Classically, randomness is generated via complex but deterministic algorithms, which are vulnerable to attacks. Quantum Random Number Generators (QRNGs) have emerged as a promising solution, as they provide true random numbers based on the intrinsic non-deterministic nature of quantum mechanics. However, critical challenges for QRNGs are the certification and quantification of their genuine randomness, especially in the presence of untrusted devices, and their compactness for systematic deployment. In this feasibility study, to face these challenges, we propose to use a silicon-photonic platform, leveraging on the concept of quantum contextuality for a semi-device independent generator. In particular, we use Klyachko-Can-Binicioglu-Shumovsky (KCBS) inequality to assess a fundamental property of quantum measurements: that their outcomes depend on the specific measurement context.
We propose a methodology to analyze a 3×3 Mach-Zenhder-based neuromorphic optical network used as a programmable logic gate. The investigated approach starts from the electromagnetic simulation of the integrated optical elements, then moves to the description of the thermal heaters including thermal cross-talk, and finally addresses the definition of the logical levels.
Machine learning (ML) is becoming a ubiquitous and powerful tool helping to address challenges in countless fields. Applications of ML addressing optics challenges have been extensively studied in recent years opening up new research directions. In particular, here, we review some of our current efforts and provide examples of successful applications of ML to the characterization of photonic devices, design, and modeling of optical subsystems, and complete end-to-end optical system optimization. ML and statistical tools can yield additional insight from measurement data, e.g. by targeted filtering of noise sources. They have also been shown to assist complex or inaccurate physics-based models through black and grey-box modeling of photonics components or subsystems. Such ML-aided models have enabled easier optimization and design (including inverse design) of optical systems.
KEYWORDS: Neural networks, Signal detection, Data centers, Signal processing, Receivers, Optoelectronics, Numerical analysis, Data communications, Computer architecture
The substantial increase in communication throughput driven by the ever-growing machine-to-machine communication within a data center and between data centers is straining the short-reach communication links. To satisfy such demand - while still complying with the strict requirements in terms of energy consumption and latency - several directions are being investigated with a strong focus on equalization techniques for intensity- modulation/direct-detection (IM/DD) transmission. In particular, the key challenge equalizers need to address is the inter-symbol interference introduced by the fiber dispersion when making use of the low-loss transmission window at 1550 nm. Standard digital equalizers such as feed-forward equalizers (FFEs) and decision-feedback equalizers (DFEs) can provide only limited compensation. Therefore more complex approaches either relying on maximum likelihood sequence estimation (MLSE) or using machine-learning tools, such as neural network (NN) based equalizers, are being investigated. Among the different NN architectures, the most promising approaches are based on NNs with memory such as time-delay feedforward NN (TD-FNN), recurrent NN (RNN), and reservoir computing (RC). In this work, we review our recent numerical results on comparing TD-FNN and RC equalizers, and benchmark their performance for 32-GBd on-off keying (OOK) transmission. A special focus will be dedicated to analyzing the memory properties of the reservoir and its impact on the full system performance. Experimental validation of the numerical findings is also provided together with reviewing our recent proposal for a new receiver architecture relying on hybrid optoelectronic processing. By spectrally slicing the received signal, independently detecting the slices and jointly processing them with an NN-based equalizer (wither TD-FNN or RC), significant extension reach is shown both numerically and experimentally.
We experimentally demonstrated 10 GHz frequency comb spectral broadening in an AlGaAsOI nano-waveguide with the peak power of only several watts. The spectral broadened 10 GHz frequency comb has high optical signal to noise ratio (OSNR) at the output of the nano-waveguide. As far as we know, it is the first photonic chip based frequency comb, relying on spectral broadening of a 10 GHz mode-locked laser comb in an AlGaAsOI nano-waveguide, with a sufficient comb output power to support several hundred Tbit/s optical data.
Space division multiplexing (SDM) is currently widely investigated in order to provide enhanced capacity thanks to the utilization of space as a new degree of multiplexing freedom in both optical fiber communication and on-chip interconnects. Basic components allowing the processing of spatial modes are critical for SDM applications. Here we present such building blocks implemented on the silicon-on-insulator (SOI) platform. These include fabrication tolerant wideband (de)multiplexers, ultra-compact mode converters and (de)multiplexers designed by topology optimization, and mode filters using one-dimensional (1D) photonic crystal silicon waveguides. We furthermore use the fabricated devices to demonstrate on-chip point-to-point mode division multiplexing transmission, and all-optical signal processing by mode-selective wavelength conversion. Finally, we report an efficient silicon photonic integrated circuit mode (de)multiplexer for few-mode fibers (FMFs).
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