Deep neural networks usually process information through multiple hidden layers. However, most hardware reservoir computing recurrent networks only have one hidden reservoir layer, which significantly limits the capability of solving practical complex tasks. Here we show a deep photonic reservoir computing (PRC) architecture, which is constructed by cascading injection-locked semiconductor lasers. In particular, the connection between successive hidden layers is all optical, without any optical-electrical conversion or analog-digital conversion. The proof of concept PRC consisting of 4 hidden layers and a total of 320 interconnected neurons (80 neurons per layer) is demonstrated in experiment. The deep PRC is applied in solving the real-world problem of signal equalization in an optical fiber communication system. It is found that the deep PRC exhibits strong capability in compensating for the nonlinear impairment of optical fibers.

Chaos in nonlinear dynamical systems is featured with irregular appearance and with high sensitivity to initial conditions. Near-infrared light chaos based on semiconductor lasers has been extensively studied and has enabled various applications. Here, we report a fully-developed hyperchaos in the mid-infrared regime, which is produced from interband cascade lasers subject to the external optical feedback. Lyapunov spectrum analysis demonstrates that the chaos exhibits three positive Lyapunov exponents. Particularly, the chaotic signal covers a broad frequency range up to the GHz level, which is two to three orders of magnitude broader than existed mid-infrared chaos solutions. The interband cascade lasers produce either periodic oscillations or low-frequency fluctuations before bifurcating to hyperchaos. This hyperchaos source is valuable for developing long-reach secure optical communication links and remote chaotic Lidar systems, taking advantage of the high-transmission windows of the atmosphere in the mid-infrared regime.

This paper reports on the spectral linewidth of InAs/InP quantum dot distributed feedback lasers. Owing to a low inversion factor and a low linewidth enhancement factor, a narrow spectral linewidth of 160 kHz (80 kHz intrinsic linewidth) with a low sensitivity to temperature is demonstrated. When using anti-reflection coatings on both facets, narrow linewidth operation is extended to high powers, believed to be due to a reduction in the longitudinal spatial hole burning. These results confirm the high potential of quantum dot lasers for increasing transmission capacity in future coherent communication systems.

This work numerically investigates the relative intensity noise of quantum dot lasers through a rate equation model taking into account both the spontaneous emission and carrier contributions. In particular, results show that the carrier noise originating from the ground and excited states significantly enhances the relative intensity noise of the laser, while that from the carrier reservoir does not. Simulations also point out that a large energy interval between the quantum confined levels is more suitable for low intensity noise operation due to the reduced contribution from the carrier noise in the excited state. Finally, the carrier noise is found to have little impact on the frequency noise thus being negligible for the investigation of the spectral linewidth. Overall, this work is useful for designing low noise quantum dot oscillators for high-speed communications, optical frequency combs and radar applications.

Time-delay reservoir computing uses a nonlinear node associated with a feedback loop to construct a large number of virtual neurons in the neural network. The clock cycle of the computing network is usually synchronous with the delay time of the feedback loop, which substantially constrains the flexibility of hardware implementations. This work shows an asynchronous reservoir computing network based on a semiconductor laser with an optical feedback loop, where the clock cycle (20 ns) is considerably different to the delay time (77 ns). The performance of this asynchronous network is experimentally investigated under various operation conditions. It is proved that the asynchronous reservoir computing shows highly competitive performance on the prediction task of Santa Fe chaotic time series, in comparison with the synchronous counterparts.

A semiconductor laser with optical feedback and optical injection is an appealing scheme to construct the time-delay reservoir computing (TDRC) networks. Quantum dot (QD) lasers are compatible to the silicon platform, and hence is helpful to develop fully on-chip TDRCs. This work theoretically demonstrates a parallel TDRC based on a Fabry-Perot QD laser with multiple longitudinal modes. These modes act as connected physical neurons, which process the input signal in parallel. The interaction strength of the modes is characterized by the cross-gain saturation effect. We show that the neuron interaction strength affects the performance of various benchmark tasks, including the memory capacity, time series prediction, nonlinear channel equalization, and spoken digit recognition. In comparison with the one-channel TDRC with the same number of nodes, the parallel TDRC runs faster and its performance is improved on multiple benchmark tasks.