Search

Abstract The history dependence of physical systems is promising for solving complex problems. To exploit spontaneous nanometre-scale optical memory for computation, the history dependence must be elucidated. Here, we demonstrate history-dependent or dynamic nano-photoisomerization through the sequential formation of multiple memory pathways in photochromic crystals via optical near-field interactions. Incident photons passing through photoisomerization memory pathways are observed using a double-probe optical near-field microscope, with one probe located on the front surface for local excitation and the other on the rear surface for near-field observations. By performing localised near-field excitation twice but at spatially different positions, negatively correlated near-field output patterns are observed between the first and second memory pathways. We also confirm that the first memory pathway is preserved after the formation of the second memory pathway. This result indicates that photoisomerization using an optical near field in diarylethene crystals has a history dependence, leading to brain-like dynamic information memorisation.

Abstract With the end of Moore’s Law and the increasing demand for computing, photonic accelerators are garnering considerable attention. This is due to the physical characteristics of light, such as high bandwidth and multiplicity, and the various synchronization phenomena that emerge in the realm of laser physics. These factors come into play as computer performance approaches its limits. In this study, we explore the application of a laser network, acting as a photonic accelerator, to the competitive multi-armed bandit problem. In this context, conflict avoidance is key to maximizing environmental rewards. We experimentally demonstrate cooperative decision-making using zero-lag and lag synchronization within a network of four semiconductor lasers. Lag synchronization of chaos realizes effective decision-making and zero-lag synchronization is responsible for the realization of the collision avoidance function. We experimentally verified a low collision rate and high reward in a fundamental 2-player, 2-slot scenario, and showed the scalability of this system. This system architecture opens up new possibilities for intelligent functionalities in laser dynamics.

The effectiveness of laser chaos decision-makers in facilitating ultrafast decision-making makes it possible for a real-time process of channel allocation scheme for the non-orthogonal multiple access (NOMA) technology in the next generation of wireless communications. However, managing the increasing number of users is challenging as the complexity of channel allocation increases significantly. To resolve this challenge, this paper proposes a novel approach to address scalability problems by introducing a parallel bandit architecture using an array of laser chaos decision-makers. In the proposed method, each user is allocated a specific channel by a dedicated laser chaos decision-maker, thereby reducing the number of available options compared with the conventional approach. This parallel bandit architecture enables the system to efficiently manage increasing users while maintaining scalability and ultrafast channel allocation in NOMA. Additionally, fairness is considered by incorporating a logarithmic utility function for design compensation. Numerical simulation results demonstrate that the proposed method achieves higher data rate and enhanced fairness than conventional NOMA approaches such as minimum distance-NOMA (MD-NOMA), conventional-NOMA (C-NOMA), and uniformed channel gain difference-NOMA (UCGD-NOMA). Moreover, the system performance is evaluated on a larger scale, accommodating a significant number of users, with the study considering up to 64 users, surpassing the limitations of the conventional approach of one laser chaos decision-maker, which is constrained to 10 users.

Abstract Collective decision-making plays a crucial role in information and communication systems. However, decision conflicts among agents often impede the maximization of potential utilities within the system. Quantum processes have shown promise in achieving conflict-free joint decisions between two agents through the entanglement of photons or the quantum interference of orbital angular momentum (OAM). Nonetheless, previous studies have shown symmetric resultant joint decisions, which, while preserving equality, fail to address disparities. In light of global challenges such as ethics and equity, it is imperative for decision-making systems to not only maintain existing equality but also address and resolve disparities. In this study, we investigate asymmetric collective decision-making theoretically and numerically using quantum interference of photons carrying OAM or entangled photons. We successfully demonstrate the realization of asymmetry; however, it should be noted that a certain degree of photon loss is inevitable in the proposed models. We also provide an analytical formulation for determining the available range of asymmetry and describe a method for obtaining the desired degree of asymmetry.

The importance of multi-valued data transmission for improving communication throughput has become apparent with the explosive growth o communication and computing demands. Although modulation schemes with denser constellations can provide increased data rates, they require a higher signal-to-noise ratio. On the other hand, sparser constellation formats offer reliable communication thanks to their greater tolerance to noise, but the throughput is inferior to that with denser constellations. Here, we introduce an approximate computing/communication that involves the critical notion of quality of information to tolerate the trade-off between throughput and reliability, which will be important in future high-performance optical communications and interconnects. In this study, we experimentally demonstrate a dynamically configured transmission scheme where different modulation formats are adopted according to the importance of bit sequences on the basis of Radio-over-Fiber (RoF) systems in transmitting floating-point numbers. The proposed system demonstrated an increased gross throughput of at least 25.1%, while errors in the transmitted floating-point numbers were uniformly bounded.

Abstract Irregular spatial distribution of photon transmission through a photochromic crystal photoisomerized by a local optical near-field excitation was previously reported, which manifested complex branching processes via the interplay of material deformation and near-field photon transfer therein. Furthermore, by combining such naturally constructed complex photon transmission with a simple photon detection protocol, Schubert polynomials, the foundation of versatile permutation operations in mathematics, have been generated. In this study, we demonstrated an order recognition algorithm inspired by Schubert calculus using optical near-field statistics via nanometre-scale photochromism. More specifically, by utilizing Schubert polynomials generated via optical near-field patterns, we showed that the order of slot machines with initially unknown reward probability was successfully recognized. We emphasized that, unlike conventional algorithms, the proposed principle does not estimate the reward probabilities but exploits the inversion relations contained in the Schubert polynomials. To quantitatively evaluate the impact of Schubert polynomials generated from an optical near-field pattern, order recognition performances were compared with uniformly distributed and spatially strongly skewed probability distributions, where the optical near-field pattern outperformed the others. We found that the number of singularities contained in Schubert polynomials and that of the given problem or considered environment exhibited a clear correspondence, indicating that superior order recognition is attained when the singularity of the given situations is presupposed. This study paves way for physical computing through the interplay of complex natural processes and mathematical insights gained by Schubert calculus.

Abstract 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. We generate chaotic temporal waveforms using the semiconductor lasers, and each waveform is assigned to a slot machine (or choice) in the multi-armed bandit problem. The biases in the amplitudes of the chaotic waveforms are adjusted based on rewards using the tug-of-war method. Subsequently, the slot machine that yields the maximum-amplitude chaotic temporal waveform with bias is selected. The scaling properties of the correct decision-making process are examined by increasing the number of slot machines to 1024, and the scaling exponent of the power-law distribution is 0.97. We demonstrate that the proposed method outperforms existing software algorithms in terms of the scaling exponent. This result paves the way for photonic decision making in large-scale multi-armed bandit problems using photonic accelerators.

Generating paired sequences with maximal compatibility from a given set is one of the most important challenges in various applications, including information and communication technologies. However, the number of possible pairings explodes in a double factorial order as a function of the number of entities, manifesting the difficulties of finding the optimal pairing that maximizes the overall reward. In the meantime, in real-world systems, such as user pairing in non-orthogonal multiple access (NOMA), pairing often needs to be conducted at high speed in dynamically changing environments; hence, efficient recognition of the environment and finding high reward pairings are highly demanded. In this paper, we demonstrate an efficient pairing algorithm to recognize compatibilities among elements as well as to find a pairing that yields a high total compatibility. The proposed pairing strategy consists of two phases. The first is the observation phase, where compatibility information among elements is obtained by only observing the sum of rewards. We show an efficient strategy that allows obtaining all compatibility information with minimal observations. The minimum number of observations under these conditions is also discussed, along with its mathematical proof. The second is the combination phase, by which a pairing with a large total reward is determined heuristically. We transform the pairing problem into a traveling salesman problem (TSP) in a three-layer graph structure, which we call Pairing-TSP. We demonstrate heuristic algorithms in solving the Pairing-TSP efficiently. This research is expected to be utilized in real-world applications such as NOMA, social networks, among others.

Low latency and reliable information transfer are highly demanded in fifth generation (5G) and beyond 5G wireless communications. A novel delay-bounded wireless media access control (MAC) protocol called Carrier Sense Multiple Access with Arbitration Point (CSMA/AP) was established to strictly ensure the upper boundary of communication delay. CSMA/AP enables collision-free and delay-bounded communications with a simple arbitration mechanism exploiting the precise time synchronization achieved by Wireless Two-Way Interferometry (Wi-Wi). Experimental demonstration and proving the feasibility in wireless environments are among the most critical steps before any further discussion of CSMA/AP and extension to various applications can take in place. In this work described in this paper, we experimentally demonstrated the fundamental principles of CSMA/AP by constructing a star-topology wireless network using software-defined radio terminals combined with precise time synchronization devices. We show that CSMA/AP was successfully operated, even with dynamic changes of the spatial position of the terminal or the capability to accommodate mobility, thanks to the real-time adaption to the dynamically changing environment by Wi-Wi. We also experimentally confirmed that the proposed CSMA/AP principle cannot be executed without Wi-Wi, which validates the importance of precise time synchronization. This study paves the way toward realizing delay-bounded wireless communications for future low-latency and highly reliable critical applications.

Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example in reinforcement learning. However, the bandit problem deals with a static environment where the agent’s actions do not influence the reward probabilities. This study aims to extend the conventional approach to a more general type of parallel reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of the agent’s actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, a modified bandit Q-learning method, in view of a potential photonic implementation. Here, state–action pairs in the environment are regarded as slot machines in the context of the bandit problem and a change in Q-value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a parallel architecture in which multiple agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state–action pair selections among agents. We demonstrate that parallel reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.

When presented with multiple choices, we all have a preference; we may suffer loss because of conflicts arising from identical selections made by other people if we insist on satisfying only our preferences. Such a scenario is applicable when a choice cannot be divided into multiple pieces owing to the intrinsic nature of the resource. Earlier studies examined how to conduct fair joint decision-making while avoiding decision conflicts in terms of game theory when multiple players have their own deterministic preference profiles. However, probabilistic preferences appear naturally in relation to the stochastic decision-making of humans, and therefore, we theoretically derive conflict-free joint decision-making that satisfies the probabilistic preferences of all individual players. To this end, we mathematically prove conditions wherein the deviation of the resultant chance of obtaining each choice from the individual preference profile (loss) becomes zero; i.e., the satisfaction of all players is appreciated while avoiding conflicts. Further, even in scenarios where zero-loss conflict-free joint decision-making is unachievable, we present approaches to derive joint decision-making that can accomplish the theoretical minimum loss while ensuring conflict-free choices. Numerical demonstrations are presented with several benchmarks.

Photonic computing has been intensively studied to explore the ultrahigh bandwidth of lightwaves. However, electronic support is indispensable for the post-processing and control of photonic systems owing to the difficulties encountered in all-optical processing. Herein, we demonstrate an ultrafast silicon circuitry capable of conducting thresholding operations on incoming chaotically oscillating high-bandwidth signals. Such circuits are critical elements in ultrafast random-number generators and photonic reinforcement learning that exploit chaotically oscillating time series. The circuit design, including active inductors for bandwidth expansion, and proof-of-principle fabricated device operations are demonstrated using a 180 nm silicon complementary metal–oxide–semiconductor technology node.

Abstract In recent cross-disciplinary studies involving both optics and computing, single-photon-based decision-making has been demonstrated by utilizing the wave-particle duality of light to solve multi-armed bandit problems. Furthermore, entangled-photon-based decision-making has managed to solve a competitive multi-armed bandit problem in such a way that conflicts of decisions among players are avoided while ensuring equality. However, as these studies are based on the polarization of light, the number of available choices is limited to two, corresponding to two orthogonal polarization states. Here we propose a scalable principle to solve competitive decision-making situations by using the orbital angular momentum of photons based on its high dimensionality, which theoretically allows an unlimited number of arms. Moreover, by extending the Hong-Ou-Mandel effect to more than two states, we theoretically establish an experimental configuration able to generate multi-photon states with orbital angular momentum and conditions that provide conflict-free selections at every turn. We numerically examine total rewards regarding three-armed bandit problems, for which the proposed strategy accomplishes almost the theoretical maximum, which is greater than a conventional mixed strategy intending to realize Nash equilibrium. This is thanks to the quantum interference effect that achieves no-conflict selections, even in the exploring phase to find the best arms.

Matrix multiplication is important in various information-processing applications, including the computation of eigenvalues and eigenvectors, and in combinatorial optimization algorithms. Therefore, reducing the computation time of matrix products is essential to speed up scientific and practical calculations. Several approaches have been proposed to speed up this process, including GPUs, fast matrix multiplication libraries, custom hardware, and efficient approximate matrix multiplication (AMM) algorithms. However, research to date has yet to focus on accelerating AMMs for general matrices on GPUs, despite the potential of GPUs to perform fast and accurate matrix product calculations. In this paper, we propose a method for improving Monte Carlo AMMs. We also give an analytical solution for the optimal values of the hyperparameters in the proposed method. The proposed method improves the approximation of the matrix product without increasing the computation time compared to the conventional AMMs. It is also designed to work well with parallel operations on GPUs and can be incorporated into various algorithms. Finally, the proposed method is applied to a power method used for eigenvalue computation. We demonstrate that, on an NVIDIA A100 GPU, the computation time can be halved compared to the conventional power method using cuBLAS.

Abstract Collective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastructure. In a previous study, we theoretically and experimentally demonstrated that entangled photons can physically resolve the difficulty of the CMAB problem. This decision-making strategy completely avoids decision conflicts while ensuring equality. However, decision conflicts can sometimes be beneficial if they yield greater rewards than non-conflicting decisions, indicating that greedy actions may provide positive effects depending on the given environment. In this study, we demonstrate a mixed strategy of entangled- and correlated-photon-based decision-making so that total rewards can be enhanced when compared to the entangled-photon-only decision strategy. We show that an optimal mixture of entangled- and correlated-photon-based strategies exists depending on the dynamics of the reward environment as well as the difficulty of the given problem. This study paves the way for utilizing both quantum and classical aspects of photons in a mixed manner for decision making and provides yet another example of the supremacy of mixed strategies known in game theory, especially in evolutionary game theory.

Quantum walks (QWs) have a property that classical random walks (RWs) do not possess—the coexistence of linear spreading and localization—and this property is utilized to implement various kinds of applications. This paper proposes RW- and QW-based algorithms for multi-armed-bandit (MAB) problems. We show that, under some settings, the QW-based model realizes higher performance than the corresponding RW-based one by associating the two operations that make MAB problems difficult—exploration and exploitation—with these two behaviors of QWs.

Abstract By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward expectation, the correct recognition of the order of arms in terms of reward expectations is not achievable. Here, we present an algorithm where the degree of exploration is adaptively controlled based on confidence intervals that represent the estimation accuracy of reward expectations. We have demonstrated numerically that our approach did improve arm order recognition accuracy significantly, along with reduced dependence on reward environments, and the total reward is almost maintained compared with conventional MAB methods. This study applies to sectors where the order information is critical, such as efficient allocation of resources in information and communications technology.

The importance of reliable information transfer in wireless networks, especially regarding communication delay, is drastically increasing to fulfill safe and high-quality communication in the 5G and post-5G era. However, conventional media access control (MAC) protocol for wireless networks, notably Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), sometimes yields unexpectedly significant delay due to its complex arbitration mechanism assuming asynchronous communication among terminals. As the delay cannot be strictly bounded by a deterministic value, this causes a vulnerability of systems relying on wireless networks. This paper utilizes precise time synchronization achieved by Wireless Two-way Interferometry (Wi-Wi), enabling all terminals to be time-synchronized via wireless signals. We show that by an appropriate periodic assignment of each terminal’s data transmission timing, named Arbitration Point (AP), a simple arbitration algorithm obtains a strictly bounded maximum value for the delay while ensuring equalities among all participants. Furthermore, we demonstrate that the total number of terminals manageable in a star-topology wireless network significantly increases by densely packing AP timings, taking into account the spatial geometry information of terminals, which is another feature delivered by Wi-Wi measurement. In the meantime, we experimentally constructed a star-topology wired network where all terminals are time-synchronized via Wi-Wi to confirm the fundamental properties identified in the proposed arbitration protocol. This study paves a new way for future wireless networks where the delay is strictly bounded and provides the basis for ultra-reliable and high-quality information transfer functionalities by utilizing precise time synchronization and space localization (space-time synchronization).

Abstract Situations involving competition for resources among entities can be modeled by the competitive multi-armed bandit (CMAB) problem, which relates to social issues such as maximizing the total outcome and achieving the fairest resource repartition among individuals. In these respects, the intrinsic randomness and global properties of quantum states provide ideal tools for obtaining optimal solutions to this problem. Based on the previous study of the CMAB problem in the two-arm, two-player case, this paper presents the theoretical principles necessary to find polarization-entangled N-photon states that can optimize the total resource output while ensuring equality among players. These principles were applied to two-, three-, four-, and five-player cases by using numerical simulations to reproduce realistic configurations and find the best strategies to overcome potential misalignment between the polarization measurement systems of the players. Although a general formula for the N-player case is not presented here, general derivation rules and a verification algorithm are proposed. This report demonstrates the potential usability of quantum states in collective decision making with limited, probabilistic resources, which could serve as a first step toward quantum-based resource allocation systems.

Imaging through scattering media is a longstanding issue in a wide range of applications, including biomedicine, security, and astronomy. Speckle-correlation imaging is promising for noninvasively seeing through scattering media by assuming shift invariance of the scattering process called the memory effect. However, the memory effect is known to be severely limited when the medium is thick. Under such a scattering condition, speckle-correlation imaging is not practical because the correlation of the speckle decays, reducing the field of view. To address this problem, we present a method for expanding the field of view of single-shot speckle-correlation imaging by extrapolating the correlation with a limited memory effect. We derive the imaging model under this scattering condition and its inversion for reconstructing the object. Our method simultaneously estimates both the object and the decay of the speckle correlation based on the gradient descent method. We numerically and experimentally demonstrate the proposed method by reconstructing point sources behind scattering media with a limited memory effect. In the demonstrations, our speckle-correlation imaging method with a minimal lensless optical setup realized a larger field of view compared with the conventional one. This study will make techniques for imaging through scattering media more practical in various fields.

Quantum walks (QWs) exhibit different properties compared with classical random walks (RWs), most notably by linear spreading and localization. In the meantime, random walks that replicate quantum walks, which we refer to as quantum-walk-replicating random walks (QWRWs), have been studied in the literature where the eventual properties of QWRW coincide with those of QWs. However, we consider that the unique attributes of QWRWs have not been fully utilized in the former studies to obtain deeper or new insights into QWs. In this paper, we highlight the directivity of one-dimensional discrete quantum walks via QWRWs. By exploiting the fact that QWRW allows trajectories of individual walkers to be considered, we first discuss the determination of future directions of QWRWs, through which the effect of linear spreading and localization is manifested in another way. Furthermore, the transition probabilities of QWRWs can also be visualized and show a highly complex shape, representing QWs in a novel way. Moreover, we discuss the first return time to the origin between RWs and QWs, which is made possible via the notion of QWRWs. We observe that the first return time statistics of QWs are quite different from RWs, caused by both the linear spreading and localization properties of QWs.

Photonic accelerators have been intensively studied to provide enhanced information processing capability to benefit from the unique attributes of physical processes. Recently, it has been reported that chaotically oscillating ultrafast time series from a laser, called laser chaos, provides the ability to solve multi-armed bandit (MAB) problems or decision-making problems at GHz order. Furthermore, it has been confirmed that the negatively correlated time-domain structure of laser chaos contributes to the acceleration of decision-making. However, the underlying mechanism of why decision-making is accelerated by correlated time series is unknown. In this study, we demonstrate a theoretical model to account for accelerating decision-making by correlated time sequence. We first confirm the effectiveness of the negative autocorrelation inherent in time series for solving two-armed bandit problems using Fourier transform surrogate methods. We propose a theoretical model that concerns the correlated time series subjected to the decision-making system and the internal status of the system therein in a unified manner, inspired by correlated random walks. We demonstrate that the performance derived analytically by the theory agrees well with the numerical simulations, which confirms the validity of the proposed model and leads to optimal system design. This study paves the way for improving the effectiveness of correlated time series for decision-making, impacting artificial intelligence and other applications.

Fully pairing all elements of a set while attempting to maximize the total benefit is a combinatorically difficult problem. Such pairing problems naturally appear in various situations in science, technology, economics, and other fields. In our previous study, we proposed an efficient method to infer the underlying compatibilities among the entities, under the constraint that only the total compatibility is observable. Furthermore, by transforming the pairing problem into a traveling salesman problem with a multi-layer architecture, a pairing optimization algorithm was successfully demonstrated to derive a high-total-compatibility pairing. However, there is substantial room for further performance enhancement by further exploiting the underlying mathematical properties. In this study, we prove the existence of algebraic structures in the pairing problem. We transform the initially estimated compatibility information into an equivalent form where the variance of the individual compatibilities is minimized. We then demonstrate that the total compatibility obtained when using the heuristic pairing algorithm on the transformed problem is significantly higher compared to the previous method. With this improved perspective on the pairing problem using fundamental mathematical properties, we can contribute to practical applications such as wireless communications beyond 5G, where efficient pairing is of critical importance. As the pairing problem is a special case of the maximum weighted matching problem, our findings may also have implications for other algorithms on fully connected graphs.

Abstract 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.

The laser chaos decision maker has been demonstrated to enable ultra-high-speed solutions of multiarmed bandit problems or decision-making in the GHz order. However, the underlying mechanisms are not well understood. In this paper, we analyze the chaotic dynamics inherent in experimentally observed laser chaos time series via surrogate data and further accelerate the decision-making performance via parameter optimization. We first evaluate the negative autocorrelation in a chaotic time series and its impact on decision-making detail. Then, we analyze the decision-making ability using three different surrogate chaos time series to examine the underlying mechanism. We clarify that the negative autocorrelation of laser chaos improves decision-making and that the amplitude distribution of the original laser chaos time series is not optimal. Hence, we introduce a new parameter for adjusting the amplitude distribution of the laser chaos to enhance the decision-making performance. This study provides a new insight into exploiting the supremacy of chaotic dynamics in artificially constructed intelligent systems.

Abstract Reinforcement learning involves decision making in dynamic and uncertain environments and constitutes an important element of artificial intelligence (AI). In this work, we experimentally demonstrate that the ultrafast chaotic oscillatory dynamics of lasers efficiently solve the multi-armed bandit problem (MAB), which requires decision making concerning a class of difficult trade-offs called the exploration–exploitation dilemma. To solve the MAB, a certain degree of randomness is required for exploration purposes. However, pseudorandom numbers generated using conventional electronic circuitry encounter severe limitations in terms of their data rate and the quality of randomness due to their algorithmic foundations. We generate laser chaos signals using a semiconductor laser sampled at a maximum rate of 100 GSample/s, and combine it with a simple decision-making principle called tug of war with a variable threshold, to ensure ultrafast, adaptive, and accurate decision making at a maximum adaptation speed of 1 GHz. We found that decision-making performance was maximized with an optimal sampling interval, and we highlight the exact coincidence between the negative autocorrelation inherent in laser chaos and decision-making performance. This study paves the way for a new realm of ultrafast photonics in the age of AI, where the ultrahigh bandwidth of light wave can provide new value.

The occurrence of plasmon resonances on metallic nanometer-scale structures is an intrinsically nanoscale phenomenon, given that the two resonance conditions (i.e., negative dielectric permittivity and large free-space wavelength in comparison with system dimensions) are realized at the same time on the nanoscale. Resonances on surface metallic nanostructures are often experimentally found by probing the structures under investigation with radiation of various frequencies following a trial-and-error method. A general technique for the tuning of these resonances is highly desirable. In this paper we address the issue of the role of local surface patterns in the tuning of these resonances as a function of wavelength and electric field polarization. The effect of nanoscale roughness on the surface plasmon polaritons of randomly patterned gold films is numerically investigated. The field enhancement and relation to specific roughness patterns is analyzed, producing many different realizations of rippled surfaces. We demonstrate that irregular patterns act as metal–dielectric–metal local nanogaps (cavities) for the resonant plasmonic field. In turn, the numerical results are compared to experimental data obtained via aperture scanning near-field optical microscopy.

In the emerging Internet of things cyber-physical system-embedded society, big data analytics needs huge computing capability with better energy efficiency. Coming to the end of Moore’s law of the electronic integrated circuit and facing the throughput limitation in parallel processing governed by Amdahl’s law, there is a strong motivation behind exploring a novel frontier of data processing in post-Moore era. Optical fiber transmissions have been making a remarkable advance over the last three decades. A record aggregated transmission capacity of the wavelength division multiplexing system per a single-mode fiber has reached 115 Tbit/s over 240 km. It is time to turn our attention to data processing by photons from the data transport by photons. A photonic accelerator (PAXEL) is a special class of processor placed at the front end of a digital computer, which is optimized to perform a specific function but does so faster with less power consumption than an electronic general-purpose processor. It can process images or time-serial data either in an analog or digital fashion on a real-time basis. Having had maturing manufacturing technology of optoelectronic devices and a diverse array of computing architectures at hand, prototyping PAXEL becomes feasible by leveraging on, e.g., cutting-edge miniature and power-efficient nanostructured silicon photonic devices. In this article, first the bottleneck and the paradigm shift of digital computing are reviewed. Next, we review an array of PAXEL architectures and applications, including artificial neural networks, reservoir computing, pass-gate logic, decision making, and compressed sensing. We assess the potential advantages and challenges for each of these PAXEL approaches to highlight the scope for future work toward practical implementation.

Soft robotics is an emerging field of research where the robot body is composed of flexible and soft materials. It allows the body to bend, twist, and deform to move or to adapt its shape to the environment for grasping, all of which are difficult for traditional hard robots with rigid bodies. However, the theoretical basis and design principles for soft robotics are not well-founded despite their recognized importance. For example, the control of soft robots is outsourced to morphological attributes and natural processes; thus, the coupled relations between a robot and its environment are particularly crucial. In this paper, we propose a mathematical foundation for soft robotics based on category theory, a branch of abstract mathematics where any notions can be described by objects and arrows. It allows for a rigorous description of the inherent characteristics of soft robots and their relation to the environment as well as the differences compared to conventional hard robots. We present a notion called the category of mobility to well describe the subject matter. The theory has been applied to a model system and analysis to highlight the adaptation behavior observed in universal grippers, which are a typical example of soft robotics. The aim of the present study is not to offer concrete engineering solutions to existing robotics but to provide clear mathematical description of soft robots by category theory and to imply its potential abilities by a simple soft gripper demonstration. This paper paves the way to developing a theoretical background and design principles for soft robotics.

Decision making based on behavioral and neural observations of living systems has been extensively studied in brain science, psychology, neuroeconomics, and other disciplines. Decision-making mechanisms have also been experimentally implemented in physical processes, such as single photons and chaotic lasers. The findings of these experiments suggest that there is a certain common basis in describing decision making, regardless of its physical realizations. In this study, we propose a local reservoir model to account for choice-based learning (CBL). CBL describes decision consistency as a phenomenon where making a certain decision increases the possibility of making that same decision again later. This phenomenon has been intensively investigated in neuroscience, psychology, and other related fields. Our proposed model is inspired by the viewpoint that a decision is affected by its local environment, which is referred to as a local reservoir. If the size of the local reservoir is large enough, consecutive decision making will not be affected by previous decisions, thus showing lower degrees of decision consistency in CBL. In contrast, if the size of the local reservoir decreases, a biased distribution occurs within it, which leads to higher degrees of decision consistency in CBL. In this study, an analytical approach for characterizing local reservoirs is presented, as well as several numerical demonstrations. Furthermore, a physical architecture for CBL based on single photons is discussed, and the effects of local reservoirs are numerically demonstrated. Decision consistency in human decision-making tasks and in recruiting empirical data is evaluated based on the local reservoir. This foundation based on a local reservoir offers further insights into the understanding and design of decision making.

We investigate the effect of a memory parameter on the performance of adaptive decision making using a tug-of-war method with the chaotic oscillatory dynamics of a semiconductor laser. We experimentally generate chaotic temporal waveforms of the semiconductor laser with optical feedback and apply them for adaptive decision making in solving a multiarmed bandit problem that aims at maximizing the total reward from slot machines whose hit probabilities are dynamically switched. We examine the dependence of making correct decisions on different values of the memory parameter. The degree of adaptivity is found to be enhanced with a smaller memory parameter, whereas the degree of convergence to the correct decision is higher for a larger memory parameter. The relations among the adaptivity, environmental changes, and the difficulties of the problem are also discussed considering the requirement of past decisions. This examination of ultrafast adaptive decision making highlights the importance of memorizing past events and paves the way for future photonic intelligence.

The laser-induced damage threshold (LIDT) is widely used as an index for evaluating an optical component’s resistance to laser light. However, a degradation in the performance of an optical component is also caused by continuous irradiation with laser light having an energy density below the LIDT. Therefore, here we focused on the degradation in performance of an optical component caused by continuous irradiation with femtosecond laser light having a low energy density, i.e., laser-induced degradation. We performed an in situ observation and analysis of an increase in scattering light intensity in fused silica substrates. In experiments conducted using a pulsed laser with a wavelength of 800 nm, a pulse width of 160 fs and pulse repetition rate of 1 kHz, we found that the scattered light intensity increased starting from a specific accumulated fluence, namely, that the laser-induced degradation had a threshold. We evaluated the threshold fluence Ft as 6.27 J/cm2 and 9.21 J/cm2 for the fused silica substrates with surface roughnesses of 0.20 nm and 0.13 nm in Ra value, respectively, showing that the threshold decreased as the surface roughness increased. In addition, we found that the reflected light spectrum changed as degradation proceeded. We analyzed the details of the degradation by measuring instantaneous reflectance changes with a pump–probe method; we observed an increase in the generation probability of photogenerated carriers in a degraded silica substrate and a damaged silica substrate and observed a Raman signal originating from a specific molecular structure of silica. From these findings, we concluded that compositional changes in the molecular structure occurred during degradation due to femtosecond laser irradiation having an energy density below the LIDT.

Ultraflat surface substrates are required to achieve an optimal performance of future optical, electronic, or optoelectronic devices for various applications, because such surfaces reduce the scattering loss of photons, electrons, or both at the surfaces and interfaces. In this paper, we review recent progress toward the realization of ultraflat materials surfaces. First, we review the development of surface-flattening techniques. Second, we briefly review the dressed photon–phonon (DPP), a nanometric quasiparticle that describes the coupled state of a photon, an electron, and a multimode-coherent phonon. Then, we review several recent developments based on DPP-photochemical etching and desorption processes, which have resulted in angstrom-scale flat surfaces. To confirm that the superior flatness of these surfaces that originated from the DPP process, we also review a simplified mathematical model that describes the scale-dependent effects of optical near-fields. Finally, we present the future outlook for these technologies.

Solution searching that accompanies combinatorial explosion is one of the most important issues in the age of artificial intelligence. Natural intelligence, which exploits natural processes for intelligent functions, is expected to help resolve or alleviate the difficulties of conventional computing paradigms and technologies. In fact, we have shown that a single-celled organism such as an amoeba can solve constraint satisfaction problems and related optimization problems as well as demonstrate experimental systems based on non-organic systems such as optical energy transfer involving near-field interactions. However, the fundamental mechanisms and limitations behind solution searching based on natural processes have not yet been understood. Herein, we present a theoretical background of solution searching based on optical excitation transfer from a category-theoretic standpoint. One important indication inspired by the category theory is that the satisfaction of short exact sequences is critical for an adequate computational operation that determines the flow of time for the system and is termed as “short-exact-sequence-based time.” In addition, the octahedral and braid structures known in triangulated categories provide a clear understanding of the underlying mechanisms, including a quantitative indication of the difficulties of obtaining solutions based on homology dimension. This study contributes to providing a fundamental background of natural intelligence.

This paper proposes a new approach to the investigation into aesthetics. Specifically, it~argues that it is possible to explain the aesthetic and its underlying dynamic relations with an~axiomatic structure (the octahedral axiom-derived category) based on contemporary mathematics (namely category theory), and through this argument suggests the possibility for discussion about the mathematical structure of the aesthetic. If there were a way to describe the structure of the aesthetic with the language of mathematical structures and mathematical axioms---a~language completely devoid of arbitrariness---then we would make possible a universal argument about the essential human activity of ``the aesthetic'', and we would also gain a new method and viewpoint into the philosophy and meaning of the act of creating a work of art and artistic activities. This paper presents one mathematical structure as a foundation in constructing the science of dynamic aesthetics based on axiomatic functionalism, which is in turn based on a new interdisciplinary investigation into the generative structure of the aesthetic.