arXiv subjects

Ryoichi Horisaki

Publications and source records attributed to Ryoichi Horisaki.

At least 37 records · Page 2Linked to original sources

arXiv2023

Quantitative phase retrieval for Zernike phase-contrast microscopy

We present a single-image numerical phase retrieval method for Zernike phase-contrast microscopy (ZPM) that addresses halo and shade-off artifacts, as well as the weak phase condition, without requiring hardware modifications. By employing a rigorous physical model of ZPM and a gradient descent algorithm for its inversion, we achieve quantitative ZPM imaging. Our approach is experimentally validated using biological cells and its quantitative nature is confirmed through comparisons with digital holography observations.

physics.optics↗

arXiv2023

Asymmetric quantum decision-making

Collective decision-making is crucial to information and communication systems. Decision conflicts among agents hinder the maximization of potential utilities of the entire system. Quantum processes can realize conflict-free joint decisions among two agents using the entanglement of photons or quantum interference of orbital angular momentum (OAM). However, previous studies have always presented symmetric resultant joint decisions. Although this property helps maintain and preserve equality, it cannot resolve disparities. Global challenges, such as ethics and equity, are recognized in the field of responsible artificial intelligence as responsible research and innovation paradigm. Thus, decision-making systems must not only preserve existing equality but also tackle disparities. This study theoretically and numerically investigates asymmetric collective decision-making using quantum interference of photons carrying OAM or entangled photons. Although asymmetry is successfully realized, a photon loss is inevitable in the proposed models. The available range of asymmetry and method for obtaining the desired degree of asymmetry are analytically formulated.

quant-ph↗

arXiv2023

Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing

Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification. Furthermore, novel possibilities have been demonstrated, such as inferring the existence of previously unseen attractors. Sampling, in contrast, has a strong influence on such functions. Sampling is indispensable in a physical reservoir computer that uses an existing physical system as a reservoir because the use of an external digital system for the data input is usually inevitable. This study analyzes the effect of sampling on the ability of reservoir computing to autonomously regenerate chaotic time series. We found, as expected, that excessively coarse sampling degrades the system performance, but also that excessively dense sampling is unsuitable. Based on quantitative indicators that capture the local and global characteristics of attractors, we identify a suitable window of the sampling frequency and discuss its underlying mechanisms.

cs.LG↗

arXiv2023

Skeleton structure inherent in discrete-time quantum walks

In this paper, we claim that a common underlying structure--a skeleton structure--is present behind discrete-time quantum walks (QWs) on a one-dimensional lattice with a homogeneous coin matrix. This skeleton structure is independent of the initial state, and partially, even of the coin matrix. This structure is best interpreted in the context of quantum-walk-replicating random walks (QWRWs), i.e., random walks that replicate the probability distribution of quantum walks, where this newly found structure acts as a simplified formula for the transition probability. Additionally, we construct a random walk whose transition probabilities are defined by the skeleton structure and demonstrate that the resultant properties of the walkers are similar to both the original QWs and QWRWs.

math-ph↗

arXiv2023

Information-theoretical analysis of statistical measures for multiscale dynamics

Multiscale entropy (MSE) has been widely used to examine nonlinear systems involving multiple time scales, such as biological and economic systems. Conversely, Allan variance has been used to evaluate the stability of oscillators, such as clocks and lasers, ranging from short to long time scales. Although these two statistical measures were developed independently for different purposes in different fields in the literature, their interest is to examine multiscale temporal structures of physical phenomena under study. We show that, from an information-theoretical perspective, they share some foundations and exhibit similar tendencies. We experimentally confirmed that similar properties of the MSE and Allan variance can be observed in low-frequency fluctuations (LFF) in chaotic lasers and physiological heartbeat data. Furthermore, we calculated the condition under which this consistency between the MSE and Allan variance exists, which is related to certain conditional probabilities. Heuristically, physical systems in nature including the aforementioned LFF and heartbeat data mostly satisfy this condition, and hence the MSE and Allan variance demonstrate similar properties. As a counterexample, an artificially constructed random sequence is demonstrated, for which the MSE and Allan variance exhibit different trends.

nlin.CD↗

arXiv2023

Analysis of temporal structure of laser chaos by Allan variance

Allan variance has been widely utilized in evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multi-scale 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 purposes. Here we demonstrate 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. Whereas the detection of LFFs is not easy with the conventional power spectrum analysis method in the low-frequency regime, we show that the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, like LFFs. This study demonstrates that Allan variance can help in understanding and characterizing versatile laser dynamics, including LFFs, which involve dynamics spanning a wide range of time scales.

physics.optics↗

arXiv2022

Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation

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 of reinforcement learning. However, the bandit problem deals with a static environment where the agent's action does not influence the reward probabilities. This study aims to extend the conventional approach to a more general multi-agent 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 agents' 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, discontinuous bandit Q-learning, 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 an updated amount of 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 multi-agent architecture in which 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 multi-agent reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.

cs.AI↗

arXiv2022

Conflict-free joint sampling for preference satisfaction through quantum interference

Collective decision-making is vital for recent information and communications technologies. In our previous research, we mathematically derived conflict-free joint decision-making that optimally satisfies players' probabilistic preference profiles. However, two problems exist regarding the optimal joint decision-making method. First, as the number of choices increases, the computational cost of calculating the optimal joint selection probability matrix explodes. Second, to derive the optimal joint selection probability matrix, all players must disclose their probabilistic preferences. Now, it is noteworthy that explicit calculation of the joint probability distribution is not necessarily needed; what is necessary for collective decisions is sampling. This study examines several sampling methods that converge to heuristic joint selection probability matrices that satisfy players' preferences. We show that they can significantly reduce the above problems of computational cost and confidentiality. We analyze the probability distribution each of the sampling methods converges to, as well as the computational cost required and the confidentiality secured. In particular, we introduce two conflict-free joint sampling methods through quantum interference of photons. The first system allows the players to hide their choices while satisfying the players' preferences almost perfectly when they have the same preferences. The second system, where the physical nature of light replaces the expensive computational cost, also conceals their choices under the assumption that they have a trusted third party. This paper has been published in Phys. Rev. Applied 18, 064018 (2022) (DOI: 10.1103/PhysRevApplied.18.064018).

quant-ph↗

arXiv2022

Experimental Demonstration of Delay-Bounded Wireless Network Based on Precise Time Synchronization

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.

cs.NI↗

arXiv2022

Pairing optimization via statistics: Algebraic structure in pairing problems and its application to performance enhancement

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.

cs.DS↗

arXiv2022

Mathematical modeling of morphological changes in photochromic crystals by catastrophe theory

Photochromic diarylethene is known to exhibit reversible photoisomerization under irradiation with ultraviolet (UV) and visible light. Besides reversible optical properties upon light irradiation, a variety of discontinuous morphological changes are reported in the literature, such as sudden crystal bending, cracking, and photosalient effects, which are caused simply by UV and visible light irradiation. These morphological phenomena with discontinuities are micro-scale changes caused by photoisomerization at the nanoscale and lead to the realization of important functions as optical devices. However, the theoretical models behind these phenomena are not well understood. In this paper, we construct a mathematical model that can treat diverse phenomena in a unified model by using swallow-tail catastrophe, a higher-order catastrophe than cusp catastrophe, from the seven elementary catastrophes that can describe discontinuities in the phenomena. By introducing the hyperbolic operating curves in the model, intrinsic properties of the photochromic crystals are represented. The induced morphogenesis, such as bending, cracking, and photosalient, are systematically classified by the proposed catastrophe model, which even implies unexplored operating conditions of the crystals and explains known phenomena. The proposed catastrophe-theory-based modeling provides a foundation for understanding and discovering the versatile morphogenesis in photochromic crystals. Furthermore, the proposed approach provides a basis for understanding and discovering various morphological changes in photochromic crystals and similar systems.

physics.optics↗

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

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

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

Roadmap on Wavefront Shaping and deep imaging in complex media

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working on this field, that has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.

physics.optics↗