Miguel C. Soriano

Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing

Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback. We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.

Parallel photonic information processing at gigabyte per second data rates using transient states

The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1 Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

Time Scales of a Chaotic Semiconductor Laser With Optical Feedback Under the Lens of a Permutation Information Analysis

We analyze the intrinsic time scales of the chaotic dynamics of a semiconductor laser subject to optical feedback by estimating quantifiers derived from a permutation information approach. Based on numerically and experimentally obtained times series, we find that permutation entropy and permutation statistical complexity allow the extraction of important characteristics of the dynamics of the system. We provide evidence that permutation statistical complexity is complementary to permutation entropy, giving valuable insights into the role of the different time scales involved in the chaotic regime of the semiconductor laser dynamics subject to delay optical feedback. The results obtained confirm that this novel approach is a conceptually simple and computationally efficient method to identify the characteristic time scales of this relevant physical system.

Dynamics of a semiconductor laser with polarization-rotated feedback and its utilization for random bit generation

Chaotic semiconductor lasers have been proven attractive for fast random bit generation. To follow this strategy, simple robust systems and a systematic approach determining the required dynamical properties and most suitable conditions for this application are needed. We show that dynamics of a single mode laser with polarization-rotated feedback are optimal for random bit generation when characterized simultaneously by a broad power spectrum and low autocorrelation. We observe that successful random bit generation also is sensitive to digitization and postprocessing procedures. Applying the identified criteria, we achieve fast random bit generation rates (up to 4 Gbit/s) with minimal postprocessing.

Optoelectronic reservoir computing: tackling noise-induced performance degradation

We present improved strategies to perform photonic information processing using an optoelectronic oscillator with delayed feedback. In particular, we study, via numerical simulations and experiments, the influence of a finite signal-to-noise ratio on the computing performance. We illustrate that the performance degradation induced by noise can be compensated for via multi-level pre-processing masks.

Fast Random Bit Generation Using a Chaotic Laser: Approaching the Information Theoretic Limit

We design and implement a chaotic-based system, enabling ultra-fast random bit sequence generation. The potential of this system to realize bit rates of 160 Gb/s for 8-bit digitization and 480 Gb/s for 16-bit digitization is demonstrated. In addition, we provide detailed insight into the interplay of dynamical properties, acquisition conditions, and post-processing, using simple and robust procedures. We employ the chaotic output of a semiconductor laser subjected to polarization-rotated feedback. We show that not only dynamics affect the randomness of the bits, but also the digitization conditions and postprocessing must be considered for successful random bit generation. Applying these general guidelines, extensible to other chaos-based systems, we can define the optimal conditions for random bit generation. We experimentally demonstrate the relevance of these criteria by extending the bit rate of our random bit generator by about two orders of magnitude. Finally, we discuss the information theoretic limits, showing that following our approach we reach the maximum possible generation rate.

Role of the phase in the identification of delay time in semiconductor lasers with optical feedback.

We consider a semiconductor laser with external optical feedback operating at a regime for which the delay time signature is extremely difficult to identify from the analysis of the intensity time series, using standard techniques. We show that such a delay signature can be successfully retrieved by computing the same quantifiers from the phase, the real or the imaginary part of the field, even in the presence of noise. Therefore, the choice of the observable is the determinant for parameter identification.

Characterizing the Hyperchaotic Dynamics of a Semiconductor Laser Subject to Optical Feedback Via Permutation Entropy

The time evolution of the output of a semiconductor laser subject to delayed optical feedback can exhibit high-dimensional chaotic fluctuations. In this contribution, our aim is to quantify the degree of unpredictability of this hyperchaotic time evolution. To that end, we estimate permutation entropy, a novel information-theory-derived quantifier particularly robust in a noisy environment. The permutation entropy is defined as a functional of a symbolic probability distribution, evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the chaotic system. This measure quantifies the diversity of orderings present in the associated time series. In order to evaluate the performance of this novel quantifier, we compare with the results obtained by using a more standard chaos quantifier, namely the Kolmogorov-Sinai entropy. Here, we present numerical results showing that the permutation entropy, evaluated at specific time-scales involved in the chaotic regime of the semiconductor laser subject to optical feedback, give valuable information about the degree of unpredictability of the chaotic laser dynamics. The influence of additive observational noise on the proposed tool is also investigated.

Security Implications of Open- and Closed-Loop Receivers in All-Optical Chaos-Based Communications

We numerically find that higher privacy and security in all-optical chaos-based communication systems can be achieved when the closed-loop scheme is used in the receiver architecture, instead of the more traditional open-loop scheme. Our results show that the extraction of the encoded message demands a larger amplitude of the message when the open-loop receiver is used than when the closed loop is implemented. A large amplitude of the encoded message compromises the performance and security of the system.

Information Processing Using Transient Dynamics of Semiconductor Lasers Subject to Delayed Feedback

The increasing amount of data being generated in different areas of science and technology require novel and efficient techniques of processing, going beyond traditional concepts. In this paper, we numerically study the information processing capabilities of semiconductor lasers subject to delayed optical feedback. Based on the recent concept of reservoir computing, we show that certain tasks, which are inherently hard for traditional computers, can be efficiently tackled by such systems. Major advantages of this approach comprise the possibility of simple and low-cost hardware implementation of the reservoir and ultrafast processing speed. Experimental results corroborate the numerical predictions.