Entropy analysis on chaos excited through destabilization of semiconductor lasers at period-one nonlinear dynamics for physical random number generation.

This study analyzes entropy of broadband chaos excited in a semiconductor laser subject to intensity-modulated optical injection for random number generation with guaranteed unpredictability. It is identified that the flattening of spectral profile around the laser relaxation resonance blurs the periodicity it brings, and thus leads to a high entropy value and a high random number generation rate. The effect of measurement device noise on entropy suggests that both the power of chaos needs to be kept at a level to achieve an adequate signal-to-noise ratio, 24 dB or more, and the entropy contribution of the measurement device noise is excluded in order to assert entropy that can be extracted solely from the intrinsic property of chaos.

Experimental control of mode-competition dynamics in a chaotic multimode semiconductor laser for decision making.

Photonic computing is widely used to accelerate the computational performance in machine learning. Photonic decision making is a promising approach utilizing photonic computing technologies to solve the multi-armed bandit problems based on reinforcement learning. Photonic decision making using chaotic mode-competition dynamics has been proposed.

Chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection.

Photonic computing has attracted increasing interest for the acceleration of information processing in machine learning applications. The mode-competition dynamics of multimode semiconductor lasers are useful for solving the multi-armed bandit problem in reinforcement learning for computing applications. In this study, we numerically evaluate the chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection.

Analysis of temporal structure of laser chaos by Allan variance.

Allan variance has been widely utilized for evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multiscale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers- not just for conventional phase stability analysis. In the present study, we demonstrated Allan variance analysis of the complex time series generated by a semiconductor laser with delayed feedback, including low-frequency fluctuations (LFFs), which exhibit both fast and slow dynamics.

Controlling chaotic itinerancy in laser dynamics for reinforcement learning.

Photonic artificial intelligence has attracted considerable interest in accelerating machine learning; however, the unique optical properties have not been fully used for achieving higher-order functionalities. Chaotic itinerancy, with its spontaneous transient dynamics among multiple quasi-attractors, can be used to realize brain-like functionalities. In this study, we numerically and experimentally investigate a method for controlling the chaotic itinerancy in a multimode semiconductor laser to solve a machine learning task, namely, the multiarmed bandit problem, which is fundamental to reinforcement learning.

Universal Single-Mode Lasing in Fully Chaotic Billiard Lasers.

By numerical simulations and experiments of fully chaotic billiard lasers, we show that single-mode lasing states are stable, whereas multi-mode lasing states are unstable when the size of the billiard is much larger than the wavelength and the external pumping power is sufficiently large. On the other hand, for integrable billiard lasers, it is shown that multi-mode lasing states are stable, whereas single-mode lasing states are unstable. These phenomena arise from the combination of two different nonlinear effects of mode-interaction due to the active lasing medium and deformation of the billiard shape.

Parallel and deep reservoir computing using semiconductor lasers with optical feedback.

Photonic reservoir computing has been intensively investigated to solve machine learning tasks effectively. A simple learning procedure of output weights is used for reservoir computing. However, the lack of training of input-node and inter-node connection weights limits the performance of reservoir computing.

Reservoir computing based on an external-cavity semiconductor laser with optical feedback modulation.

We numerically and experimentally investigate reservoir computing based on a single semiconductor laser with optical feedback modulation. In this scheme, an input signal is injected into a semiconductor laser via intensity or phase modulation of the optical feedback signal. We perform a chaotic time-series prediction task using the reservoir and compare the performances of intensity and phase modulation schemes.

Decision making for large-scale multi-armed bandit problems using bias control of chaotic temporal waveforms in semiconductor lasers.

Decision making using photonic technologies has been intensively researched for solving the multi-armed bandit problem, which is fundamental to reinforcement learning. However, these technologies are yet to be extended to large-scale multi-armed bandit problems. In this study, we conduct a numerical investigation of decision making to solve large-scale multi-armed bandit problems by controlling the biases of chaotic temporal waveforms generated in semiconductor lasers with optical feedback.

Photonic reinforcement learning based on optoelectronic reservoir computing.

Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, internet advertising, and elastic optical networks. However, the computational cost of reinforcement learning with deep neural networks is extremely high and reducing the learning cost is a challenging issue. We propose a photonic on-line implementation of reinforcement learning using optoelectronic delay-based reservoir computing, both experimentally and numerically.

High-entropy chaos generation using semiconductor lasers subject to intensity-modulated optical injection for certified physical random number generation.

This study investigates high-entropy chaos generation using a semiconductor laser subject to intensity-modulated optical injection for certified physical random number generation. Chaos with a continuous spectral profile that is not only widely distributed but also broadly flattened over a bandwidth of 33 GHz is generated. The former suggests that the chaos can be sampled at a high rate while keeping sufficient un-correlation between data samples, and the latter indicates that the chaos possesses high entropy, both of which enhance the generation rate of physical random numbers with guaranteed unpredictability.

Fast dynamics of low-frequency fluctuations in a quantum-dot laser with optical feedback.

We experimentally investigate the complex dynamics of a multi-mode quantum-dot semiconductor laser with time-delayed optical feedback. We examine a two-dimensional bifurcation diagram of the quantum-dot laser as a comprehensive dynamical map by changing the injection current and feedback strength. We found that the bifurcation diagram contains two different parameter regions of low-frequency fluctuations.

Entropy rate of chaos in an optically injected semiconductor laser for physical random number generation.

We evaluate the (ɛ, τ) entropy of chaotic laser outputs generated by an optically injected semiconductor laser for physical random number generation. The vertical resolution ɛ and sampling time τ are numerically optimized by comparing the (ɛ, τ) entropy with the Kolmogorov-Sinai entropy, which is estimated from the Lyapunov exponents using linearized model equations. We then investigate the dependence of the (ɛ, τ) entropy on the optical injection strength of the laser system.

Laser network decision making by lag synchronization of chaos in a ring configuration.

Photonic technologies are promising for solving complex tasks in artificial intelligence. In this paper, we numerically investigate decision making for solving the multi-armed bandit problem using lag synchronization of chaos in a ring laser-network configuration. We construct a laser network consisting of unidirectionally coupled semiconductor lasers, whereby spontaneous exchange of the leader-laggard relationship in the lag synchronization of chaos is observed.

Using multidimensional speckle dynamics for high-speed, large-scale, parallel photonic computing.

The recent rapid increase in demand for data processing has resulted in the need for novel machine learning concepts and hardware. Physical reservoir computing and an extreme learning machine are novel computing paradigms based on physical systems themselves, where the high dimensionality and nonlinearity play a crucial role in the information processing. Herein, we propose the use of multidimensional speckle dynamics in multimode fibers for information processing, where input information is mapped into the space, frequency, and time domains by an optical phase modulation technique.

Adaptive model selection in photonic reservoir computing by reinforcement learning.

Photonic reservoir computing is an emergent technology toward beyond-Neumann computing. Although photonic reservoir computing provides superior performance in environments whose characteristics are coincident with the training datasets for the reservoir, the performance is significantly degraded if these characteristics deviate from the original knowledge used in the training phase. Here, we propose a scheme of adaptive model selection in photonic reservoir computing using reinforcement learning.

Entropy evaluation of white chaos generated by optical heterodyne for certifying physical random number generators.

The entropy of white chaos is evaluated to certify physical random number generators. White chaos is generated from the electric subtraction of two optical heterodyne signals of two chaotic outputs in semiconductor lasers with optical feedback. We use the statistical test suites of NIST Special Publication 800-90B for the evaluation of physical entropy sources of white chaos with an eight-bit resolution.

Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series.

Dynamic channel selection is among the most important wireless communication elements in dynamically changing electromagnetic environments wherein, a user can experience improved communication quality by choosing a better channel. Multi-armed bandit (MAB) algorithms are a promising approach that resolve the trade-off between channel exploration and exploitation of enhanced communication quality. Ultrafast solution of MAB problems has been demonstrated by utilizing chaotically oscillating time series generated by semiconductor lasers.

Decision making for the multi-armed bandit problem using lag synchronization of chaos in mutually coupled semiconductor lasers.

We numerically and experimentally demonstrate the utilization of the synchronization of chaotic lasers for decision making. We perform decision making to solve the multi-armed bandit problem using lag synchronization of chaos in mutually coupled semiconductor lasers. We observe the spontaneous exchanges of the leader-laggard relationship under lag synchronization of chaos, and we find that the leader laser can be controlled by changing the coupling strengths between the two lasers.

Generative adversarial network based on chaotic time series.

Generative adversarial networks (GANs) are becoming increasingly important in the artificial construction of natural images and related functionalities, wherein two types of networks called generators and discriminators evolve through adversarial mechanisms. Using deep convolutional neural networks and related techniques, high-resolution and highly realistic scenes, human faces, etc. have been generated.

On-chip photonic decision maker using spontaneous mode switching in a ring laser.

Efficient and accurate decision making is gaining increased importance with the rapid expansion of information communication technologies including artificial intelligence. Here, we propose and experimentally demonstrate an on-chip, integrated photonic decision maker based on a ring laser. The ring laser exhibits spontaneous switching between clockwise and counter-clockwise oscillatory dynamics; we utilize such nature to solve a multi-armed bandit problem.

Compact reservoir computing with a photonic integrated circuit.

Photonic reservoir computing is a new paradigm for performing high-speed prediction and classification tasks in an efficient manner. The major challenge for the miniaturization of photonic reservoir computing is the need for the use of photonic integrated circuits. Herein, we experimentally demonstrate reservoir computing using a photonic integrated circuit with a semiconductor laser and a short external cavity.

Spontaneous exchange of leader-laggard relationship in mutually coupled synchronized semiconductor lasers.

We investigate the instantaneous behavior of synchronized temporal wave forms in two mutually coupled semiconductor lasers numerically and experimentally. The temporal wave forms of two lasers are synchronized with a propagation delay time, with one laser oscillating in advance of the other, known as the leader-laggard relationship. The leader-laggard relationship can be determined by measuring the cross-correlation between the two temporal wave forms with the propagation delay time.

Photonic integrated circuits unveil crisis-induced intermittency.

We experimentally investigate an intermittent route to chaos in a photonic integrated circuit consisting of a semiconductor laser with time-delayed optical feedback from a short external cavity. The transition from a period-doubling dynamics to a fully-developed chaos reveals a stage intermittently exhibiting these two dynamics. We unveil the bifurcation mechanism underlying this route to chaos by using the Lang-Kobayashi model and demonstrate that the process is based on a phenomenon of attractor expansion initiated by a particular distribution of the local Lyapunov exponents.

Laser dynamical reservoir computing with consistency: an approach of a chaos mask signal.

We numerically investigate reservoir computing based on the consistency of a semiconductor laser subjected to optical feedback and injection. We introduce a chaos mask signal as an input temporal mask for reservoir computing and perform a time-series prediction task. We compare the errors of the task obtained from the chaos mask signal with those obtained from other digital and analog masks.