We introduce the use of Fabry-Perot (FP) lasers as potential neuromorphic computing machines with parallel processing capabilities. With the use of optical injection between a master FP laser and a slave FP laser under feedback we demonstrate the potential for scaling up the processing power at longitudinal mode granularity and perform real-time processing for signal equalization in 25 Gbaud intensity modulation direct detection optical communication systems. We demonstrate the improvement of classification performance as the number of modes multiplies the number of virtual nodes and offers the capability of simultaneous processing of arbitrary data streams. Extensive numerical simulations show that up to 8 longitudinal modes in typical Fabry-Perot lasers can be leveraged to enhance classification performance.

We present a photonic approach for fast quantum random number generation based on optically sampled amplified spontaneous emission (ASE). This approach utilizes a terahertz optical asymmetric demultiplexer to sample the ASE and then digitize the sampled optical pulses into random bits using a multi-bit parallel comparator. A proof-of-concept experiment demonstrates that 40 Gb/s random bits with verified randomness can be obtained using our method. The current generation rate is mainly limited by the bandwidth of the available ASE source.

Information-theoretically provable unique true random numbers, which cannot be correlated or controlled by an attacker, can be generated based on quantum measurement of vacuum state and universal-hashing randomness extraction. Quantum entropy in the measurements decides the quality and security of the random number generator (RNG). At the same time, it directly determines the extraction ratio of true randomness from the raw data, in other words, it obviously affects quantum random bits generating rate. In this work, we commit to enhancing quantum entropy content in the vacuum noise based quantum RNG. We have taken into account main factors in this proposal to establish the theoretical model of quantum entropy content, including the effects of classical noise, the optimum dynamical analog-digital convertor (ADC) range, the local gain and the electronic gain of the homodyne system. We demonstrate that by amplifying the vacuum quantum noise, abundant quantum entropy is extractable in the step of post-processing even classical noise excursion, which may be deliberately induced by an eavesdropper, is large. Based on the discussion and the fact that the bandwidth of quantum vacuum noise is infinite, we propose large dynamical range and moderate TIA gain to pursue higher local oscillator (LO) amplification of vacuum quadrature and broader detection bandwidth in homodyne system. High true randomness extraction ratio together with high sampling rate is attainable. Experimentally, an extraction ratio of true randomness of 85.3% is achieved by finite enhancement of the laser power of the LO when classical noise excursions of the raw data is obvious.

Chaotic time series prediction has been paid intense attention in recent years due to its important applications. Herein, we present a single-node photonic reservoir computing approach to forecasting the chaotic behavior of external cavity semiconductor lasers using only observed data. In the reservoir, we employ a semiconductor laser with delay as the sole nonlinear physical node. By investigating the effect of the reservoir meta-parameters on the prediction performance, we numerically demonstrate that there exists an optimal meta-parameter space for forecasting optical-feedback-induced chaos. Simulation results demonstrate that using our method, the upcoming chaotic time series can be continuously predicted for a time period in excess of 2 ns with a normalized mean squared error lower than 0.1. This proposed method only utilizes simple nonlinear semiconductor lasers and thus offers a hardware-friendly approach for complex chaos prediction. In addition, this work may provide a roadmap for the meta-parameter selection of a delay-based photonic reservoir to obtain optimal prediction performance.

We propose a method to generate broadband laser chaos using a quantum cascade laser (QCL). Through numerical simulation, we give the evidence that the QCL with optical feedback can route to chaos through the quasi-periodic path. Furthermore, we investigate the influence of the feedback intensity and the bias current on the chaos bandwidth. Final results demonstrate that the chaos bandwidth can headily reach 43.1 GHz due to the lack of relaxation oscillation phenomena in QCLs.

The time delay signature is a crucial secure key for chaos communication using optical feedback semiconductor lasers. Herein, we propose a single node-based photonic reservoir computing method for extracting the time delay signature of optical-feedback-induced chaos. Through drawing the phase diagram of the chaotic time series and its delayed copies, we transform the time delay signature into the shape feature of the obtained two-dimensional image. After training the optimized photonic reservoir with these two-dimensional images, we numerically demonstrate that our method can achieve an overall extraction accuracy rate of 97.3% for different optical feedback semiconductor lasers. Considering its good performance and simple configuration, we believe that this single node-based photonic reservoir computing offers a hardware-friendly solution for time delay signature extraction.