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

Homma, Ryutaro; Kochi, Satoshi; Niiyama, Tomoaki; Mihana, Takatomo; Mitsui, Yusuke; Kanno, Kazutaka; Uchida, Atsushi; Naruse, Makoto; Sunada, Satoshi

doi:10.1038/s41598-019-45754-3

Cited by 25 publications

(17 citation statements)

References 38 publications

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“…27 . In optical domains, various methods of generating chaotic dynamics other than the delayed optical feedback employed in the present study are known 28 , such as spontaneous mode switching in ring lasers 29 , mode competition in vertical-cavity surface-emitting lasers 30 , and light–atom coupling 31,32 . Examining the compositions of these physical platforms and GANs is a topic for future study.…”

Section: Discussionmentioning

confidence: 99%

Generative adversarial network based on chaotic time series

Naruse

Matsubara

Chauvet

et al. 2019

Sci Rep

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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. GANs generally require large amounts of genuine training data sets, as well as vast amounts of pseudorandom numbers. In this study, we utilized chaotic time series generated experimentally by semiconductor lasers for the latent variables of a GAN, whereby the inherent nature of chaos could be reflected or transformed into the generated output data. We show that the similarity in proximity, which describes the robustness of the generated images with respect to minute changes in the input latent variables, is enhanced, while the versatility overall is not severely degraded. Furthermore, we demonstrate that the surrogate chaos time series eliminates the signature of the generated images that is originally observed corresponding to the negative autocorrelation inherent in the chaos sequence. We also address the effects of utilizing chaotic time series to retrieve images from the trained generator.

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Section: Discussionmentioning

confidence: 99%

Generative adversarial network based on chaotic time series

Naruse

Matsubara

Chauvet

et al. 2019

Sci Rep

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“…Ultrahigh-speed decision-maker using laser chaos has been demonstrated for solving multi-armed bandit (MAB) problems at GHz order [1][2][3][4]. The goal of MAB problems is to maximize the total reward from multiple slot machines in uncertain environments [5].…”

Section: Introductionmentioning

confidence: 99%

“…The inherent temporal dynamics of chaotic lasers accelerates the solution of MAB problems with two arms [1,2,6], followed by its scalable extensions up to 64 arms [3]. Homma et al developed a compact (5 mm 2 area) and on-chip photonic decision-maker [4].…”

Section: Introductionmentioning

confidence: 99%

Dynamic channel bonding in WLANs by hierarchical laser chaos decision maker

Kanemasa

Yusuke

et al. 2022

NOLTA

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Laser chaos decision-maker has been demonstrated to enable ultrahigh-speed decision-making in solving multi-armed bandit (MAB) problems in the GHz order. In addition to recent intensive studies of photonic information processing devices and systems, the pursuit of novel applications is important, which is also demanded from future technologies, including Beyond 5G context. In this paper, we examine the applicability to dynamic channel bonding (DCB), which has been introduced in wireless local area networks (WLANs), and demonstrate a method for achieving higher data rate transmissions while avoiding interference. First, we propose a DCB method utilizing laser chaos decision-maker. Second, we design two hierarchical trees for decision making, that is, DCB selection. Third, we experimentally implement our proposed methods in a practical WLAN and confirm its operational ability. We analyze the parameter of the proposed method and compare the proposed method with conventional decision-making algorithms of -greedy and UCB1-tuned. We show that our proposed method demonstrates better DCB decisions than the other decision-making algorithms. Furthermore, we demonstrate that in DCB, the design method of the hierarchical trees or the parameter for the proposed method influences the performance of decision making.

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“…The tug-of-war method provides faster decision making than software algorithms [7]. Recently, photonic implementations of the tug-of-war method have been demonstrated using quantum dots [11,12], single photons [13], entangled photons [14], and chaotic semiconductor lasers [15][16][17][18][19][20][21][22][23]. Furthermore, photonic decision making of the multi-armed bandit problem has been reported using chaotic temporal waveforms with a threshold [15][16][17], lag synchronization of chaos in coupled semiconductor lasers [18], laser network dynamics [19,20], and mode-switching dynamics in a ring-cavity laser [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive decision making using a chaotic semiconductor laser for multi-armed bandit problem with time-varying hit probabilities

Oda

Mihana

Kanno

et al. 2022

NOLTA

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We numerically demonstrate the principle of adaptive decision making for solving multi-armed bandit problems in dynamically changing reward environments. We use the tugof-war method by comparing a threshold and a chaotic temporal waveform generated from a semiconductor laser observed in an experiment. We propose a method for detecting dynamic changes in hit probabilities by evaluating short-term standard deviations of the estimated hit probabilities. Furthermore, the threshold is forced to be initialized when changes in the hit probabilities are detected. We perform adaptive decision making in time-varying hit probabilities, including cases in which the differences in the hit probabilities are small. The proposed method paves the way for ultrafast photonic decision making in dynamically changing environments for various applications, such as cognitive wireless communications and robot control using reinforcement learning.

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

Cited by 25 publications

References 38 publications

Generative adversarial network based on chaotic time series

Generative adversarial network based on chaotic time series

Dynamic channel bonding in WLANs by hierarchical laser chaos decision maker

Adaptive decision making using a chaotic semiconductor laser for multi-armed bandit problem with time-varying hit probabilities

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