arXiv subjects

Makoto Naruse

Publications and source records attributed to Makoto Naruse.

At least 37 records · Page 2Linked to original sources

arXiv2022

Extrapolated speckle-correlation imaging

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 non-invasively 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.

physics.optics↗

arXiv2022

Directivity of quantum walk via its random walk replica

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.

math-ph↗

arXiv2022

Theory of Acceleration of Decision Making by Correlated Time Sequences

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, provide 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. The present study paves the way for improving the effectiveness of correlated time series for decision-making, impacting artificial intelligence and other applications.

cs.LG↗

arXiv2022

Parallel bandit architecture based on laser chaos for reinforcement learning

Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of machine learning, and photonic decision-making principles have been demonstrated with respect to the multi-armed bandit problems. However, reinforcement learning could involve a massive number of states, unlike previously demonstrated bandit problems where the number of states is only one. Q-learning is a well-known approach in reinforcement learning that can deal with many states. The architecture of Q-learning, however, does not fit well photonic implementations due to its separation of update rule and the action selection. In this study, we organize a new architecture for multi-state reinforcement learning as a parallel array of bandit problems in order to benefit from photonic decision-makers, which we call parallel bandit architecture for reinforcement learning or PBRL in short. Taking a cart-pole balancing problem as an instance, we demonstrate that PBRL adapts to the environment in fewer time steps than Q-learning. Furthermore, PBRL yields faster adaptation when operated with a chaotic laser time series than the case with uniformly distributed pseudorandom numbers where the autocorrelation inherent in the laser chaos provides a positive effect. We also find that the variety of states that the system undergoes during the learning phase exhibits completely different properties between PBRL and Q-learning. The insights obtained through the present study are also beneficial for existing computing platforms, not just photonic realizations, in accelerating performances by the PBRL algorithms and correlated random sequences.

cs.ET↗

arXiv2022

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 utilized for achieving higher-order functionalities. Chaotic itinerancy, with its spontaneous transient dynamics among multiple quasi-attractors, can be employed to realize brain-like functionalities. In this paper, we propose a method for controlling the chaotic itinerancy in a multi-mode semiconductor laser to solve a machine learning task, known as the multi-armed bandit problem, which is fundamental to reinforcement learning. The proposed method utilizes ultrafast chaotic itinerant motion in mode competition dynamics controlled via optical injection. We found that the exploration mechanism is completely different from a conventional searching algorithm and is highly scalable, outperforming the conventional approaches for large-scale bandit problems. This study paves the way to utilize chaotic itinerancy for effectively solving complex machine learning tasks as photonic hardware accelerators.

physics.optics↗

arXiv2022

Efficient Pairing in Unknown Environments: Minimal Observations and TSP-based Optimization

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.

cs.DS↗

arXiv2022

Optimal preference satisfaction for conflict-free joint decisions

We all have preferences when multiple choices are available. If we insist on satisfying our preferences only, we may suffer a loss due to conflicts with other people's identical selections. Such a case applies when the choice cannot be divided into multiple pieces due to the intrinsic nature of the resources. Former studies, such as the top trading cycle, examined how to conduct fair joint decision-making while avoiding decision conflicts from the perspective of game theory when multiple players have their own deterministic preference profiles. However, in reality, probabilistic preferences can naturally appear in relation to the stochastic decision-making of humans. Here, we theoretically derive conflict-free joint decision-making that can satisfy the probabilistic preferences of all individual players. More specifically, we mathematically prove the conditions wherein the deviation of the resultant chance of obtaining each choice from the individual preference profile, which we call the loss, becomes zero, meaning that all players' satisfaction is perfectly appreciated while avoiding decision conflicts. Furthermore, even in situations where zero-loss conflict-free joint decision-making is unachievable, we show how to derive joint decision-making that accomplishes the theoretical minimum loss while ensuring conflict-free choices. Numerical demonstrations are also shown with several benchmarks.

econ.TH↗

arXiv2022

Single-shot blind deconvolution with coded aperture

In this paper, we present a method for single-shot blind deconvolution incorporating a coded aperture (CA). In this method, we utilize the CA, inserted on the pupil plane, as support constraints in blind deconvolution. Not only an object but also a point spread function of turbulence are estimated from a single captured image by a reconstruction algorithm with the CA support. The proposed method is demonstrated by a simulation and an experiment in which point sources are recovered under severe turbulence.

eess.IV↗

arXiv2021

Order recognition by Schubert polynomials generated by optical near-field statistics via nanometre-scale photochromism

We have previously observed an irregular spatial distribution of photon transmission through a photochromic crystal photoisomerized by a local optical near-field excitation, manifesting complex branching processes via the interplay of deformation of the material 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 demonstrate 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 show that the order of slot machines with initially unknown reward probability is successfully recognized. We emphasize that, unlike conventional algorithms in the literature, the proposed principle does not estimate the reward probabilities. Instead, it exploits the inversion relations contained in the Schubert polynomials. To quantitatively evaluate the impact of the Schubert polynomials generated from an optical near-field pattern, order recognition performances are compared with uniformly distributed and spatially strongly skewed probability distributions, where the optical near-field pattern outperforms the others. We found that the number of singularities contained in Schubert polynomials and that of the given problem or considered environment exhibits a clear correspondence, indicating that superior order recognition performances may be attained if the singularity of the given problem is presupposed. This study paves a new way toward nanophotonic intelligent devices and systems by the interplay of complex natural processes and mathematical insights gained by Schubert calculus.

physics.optics↗

arXiv2021

Conflict-free collective stochastic decision making by orbital angular momentum of photons through quantum interference

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.

quant-ph↗

arXiv2020

Entangled and correlated photon mixed strategy for social decision making

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.

quant-ph↗

arXiv2020

Entangled N-photon states for fair and optimal social decision making

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.

quant-ph↗

arXiv2020

Information transfer based on precision time synchronization via wireless interferometry

The growing demand of high-bandwidth and low-latency information transfer in information and communication technologies such as data centres and in-vehicle networks has increased the importance of optical communication networks in recent years. However, complicated arbitration schemes can impose significant overheads in data transfer, which may inhibit the full exploitation of the potential of optical interconnects. Herein, we propose an arbitration protocol based on precision time synchronization via wireless two-way interferometry (Wi-Wi), and numerically validate its efficiency including the ability to impose a strict upper bound on the latency of data transfer. Compared with the conventional carrier sense multiple access/collision detection (CSMA/CD)-based approach, a significant improvement in the data transfer was observed especially in the cases with high traffic flow rate. Furthermore, we conducted a proof-of-principle experiment for Wi-Wi-based data transfer between two electrically connected nodes and confirmed that the skew was less than 300 ns and remained stable over time. Conversely, non-WiWi-based data transfer exhibited huge and unstable skew. These results indicate that precision time synchronization is a promising resource to significantly reduce the communication overheads and ensure low latency for future networks and real-time applications.

cs.NI↗

arXiv2020

Arm order recognition in multi-armed bandit problem with laser chaos time series

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.

eess.SP↗

arXiv2020

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. In this scheme, a temporal waveform is generated by different dynamic source models that change over time. The system autonomously identifies the best source model for the task of time series prediction using photonic reservoir computing and reinforcement learning. We prepare two types of output weights for the source models, and the system adaptively selected the correct model using reinforcement learning, where the prediction errors are associated with rewards. We succeed in adaptive model selection when the source signal is temporally mixed, having originally been generated by two different dynamic system models, as well as when the signal is a mixture from the same model but with different parameter values. This study paves the way for autonomous behavior in photonic artificial intelligence and could lead to new applications in load forecasting and multi-objective control, where frequent environment changes are expected.

cs.ET↗

arXiv2020

Lotka-Volterra competition mechanism embedded in a decision-making method

Decision making is a fundamental capability of living organisms, and has recently been gaining increasing importance in many engineering applications. Here, we consider a simple decision-making principle to identify an optimal choice in multi-armed bandit (MAB) problems, which is fundamental in the context of reinforcement learning. We demonstrate that the identification mechanism of the method is well described by using a competitive ecosystem model, i.e., the competitive Lotka--Volterra (LV) model. Based on the "winner-take-all" mechanism in the competitive LV model, we demonstrate that non-best choices are eliminated and only the best choice survives; the failure of the non-best choices exponentially decreases while repeating the choice trials. Furthermore, we apply a mean-field approximation to the proposed decision-making method and show that the method has an excellent scalability of $O(\log N)$ with respect to the number of choices $N$. These results allow for a new perspective on optimal search capabilities in competitive systems.

physics.app-ph↗

arXiv2020

Experimental demonstration of random walk by probability chaos using single photons

In our former work (Sci. Rep. 4: 6039, 2014), we theoretically and numerically demonstrated that chaotic oscillation can be induced in a nanoscale system consisting of quantum dots between which energy transfer occurs via optical near-field interactions. Furthermore, in addition to the nanoscale implementation of oscillators, it is intriguing that the chaotic behavior is associated with probability derived via a density matrix formalism. Indeed, in our previous work (Sci. Rep. 6: 38634, 2016) we examined such oscillating probabilities via diffusivity analysis by constructing random walkers driven by chaotically driven bias. In this study, we experimentally implemented the concept of probability chaos using a single-photon source that was chaotically modulated by an external electro-optical modulator that directly yielded random walkers via single-photon observations after a polarization beam splitter. An evident signature was observed in the resulting ensemble average of the time-averaged mean square displacement. Although the experiment involved a scaled-up, proof-of-concept model of a genuine nanoscale oscillator, the experimental observations clearly validate the concept of oscillating probability, paving the way toward future ideal nanoscale systems.

physics.optics↗

arXiv2020

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 by which the difficult tradeoff between exploration to search for better a channel and exploitation to experience enhanced communication quality is resolved. Ultrafast solution of MAB problems has been demonstrated by utilizing chaotically oscillating time series generated by semiconductor lasers. In this study, we experimentally demonstrate a MAB algorithm incorporating laser chaos time series in a wireless local area network (WLAN). Autonomous and adaptive dynamic channel selection is successfully demonstrated in an IEEE802.11a-based, four-channel WLAN. Although the laser chaos time series is arranged prior to the WLAN experiments, the results confirm the usefulness of ultrafast chaotic sequences for real wireless applications. In addition, we numerically examine the underlining adaptation mechanism of the significantly simplified MAB algorithm implemented in the present study compared with the previously reported chaos-based decision makers. This study provides a first step toward the application of ultrafast chaotic lasers for future high-performance wireless communication networks.

eess.SP↗