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The photonic time-stretch technique is a single-pulse broadband spectroscopy method enabled by dispersive Fourier transformation. This technique enables an extremely high spectrum acquisition rate, determined by the repetition rates of femtosecond mode-locked lasers, which are typically in the range of tens of MHz. However, achieving this high spectrum acquisition rate necessitates a compromise in either the spectral resolution or the spectral bandwidth to prevent overlaps between adjacent stretched pulses. In this study, we introduce a method that overcomes this limitation by incorporating compressive sensing with pulse-by-pulse amplitude modulation, enabling the decomposition of excessively stretched, overlapping pulses. Through numerical evaluations of optofluidic microparticle flow analysis and high-speed gas-phase molecular spectroscopy, we demonstrate the efficacy of our noise-resilient algorithm, showcasing a severalfold increase in the spectrum acquisition rate without compromising resolution and bandwidth.
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Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing. Collective decision-making with a laser network, employing the chaotic and synchronous dynamics of optically interconnected lasers to address the competitive multi-armed bandit (CMAB) problem, is a highly compelling approach due to its scalability and experimental feasibility. We investigated essential network structures for collective decision-making through quantitative stability analysis. Moreover, we demonstrated the asymmetric preferences of players in the CMAB problem, extending its functionality to more practical applications. Our study highlights the capability and significance of machine learning built upon chaotic lasers and photonic devices.
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We present a noninvasive method for quantitative phase imaging through dynamically scattering media. A complex amplitude object, illuminated with coherent light, is captured through a dynamically scattering medium and a variable coded aperture, without the need for interferometric measurements or imaging optics. The complex amplitude of the object is computationally retrieved from intensity images that use multiple coded aperture patterns, employing a stochastic gradient descent algorithm. We demonstrate the proposed method both numerically and experimentally.
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We propose a method of computer-generated holography (CGH) using incoherent light emitted from a mobile phone screen. In this method, we suppose a cascade of holograms in which the first hologram is a color image displayed on the mobile phone screen. The hologram cascade is synthesized by solving an inverse problem with respect to the propagation of incoherent light. We demonstrate a three-dimensional color image reproduction using a two-layered hologram cascade composed of an iPhone and a spatial light modulator.
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Quantitative phase imaging (QPI), such as digital holography, is considered a promising tool in the field of life science due to its noninvasive and quantitative visualization capabilities without the need for fluorescence labeling. However, the popularity of QPI systems is limited due to the cost and complexity of their hardware. In contrast, Zernike phase-contrast microscopy (ZPM) has been widely used in practical scenarios but has not been categorized as QPI, owing to halo and shade-off artifacts and the weak phase condition. Here, we present a single-image phase retrieval method for ZPM that addresses these issues without requiring hardware modifications. By employing a rigorous physical model of ZPM and a gradient descent algorithm for its inversion, we achieve single-shot QPI with an off-the-shelf ZPM system. Our approach is validated in simulations and experiments, demonstrating QPI of a polymer microbead and biological cells. The quantitative nature of our method for single-cell imaging is confirmed through comparisons with observations from an established QPI technique conducted through digital holography. This study paves the way for transforming non-QPI ZPM systems into QPI systems.
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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.
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We present a method for speckle-correlation imaging with an extended field of view to observe spatially non-sparse objects. In speckle-correlation imaging, an object is recovered from a non-invasively captured image through a scattering medium by assuming shift-invariance of the optical process called the memory effect. The field of view of speckle-correlation imaging is limited by the size of the memory effect, and it can be extended by extrapolating the speckle correlation in the reconstruction process. However, spatially sparse objects are assumed in the inversion process because of its severe ill-posedness. To address this issue, we introduce a deep image prior, which regularizes the image statistics by using the structure of an untrained convolutional neural network, to speckle-correlation imaging. We experimentally demonstrated the proposed method and showed the possibility of extending the method to imaging through scattering media.
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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.
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We present a diffractive optics design for incoherent imaging with an extendable field-of-view. In our design method, multiple layers of diffractive optical elements (DOEs) are synthesized so that images on the input plane illuminated with spatially incoherent light are reproduced upright on the output plane. In addition, our method removes the need for an approximation of shift invariance, which has been assumed in conventional optical designs for incoherent imaging systems. Once the DOE cascade is calculated, the field-of-view can be extended by using an array of such DOEs without further calculation. We derive the optical condition to calculate the DOEs and numerically demonstrate the proposed method with the condition.
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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.
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Aprendizaje Automático , Redes Neurales de la Computación , Factores de Tiempo , ReproducciónRESUMEN
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.
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We present a method for computer-generated holography (CGH) in which different images are reproduced on both sides of a hologram with a single illumination source. In the proposed method, we use a transmissive spatial light modulator (SLM) and a half mirror (HM) located downstream of the SLM. The light modulated by the SLM is partially reflected by the HM, and the reflected light is modulated again by the SLM for the double-sided image reproduction. We derive an algorithm for double-sided CGH and experimentally demonstrate it.
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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, their interest lies in examining the multiscale temporal structures of physical phenomena under study. We demonstrate 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, natural physical systems including the aforementioned LFF and heartbeat data mostly satisfy this condition, and hence, the MSE and Allan variance demonstrate similar properties. As a counterexample, we demonstrate an artificially constructed random sequence, for which the MSE and Allan variance exhibit different trends.
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Allan variance has been widely utilized for evaluating the stability of the time series generated by atomic clocks and lasers, in time regimes ranging from short to extremely long. This multiscale examination capability of the Allan variance may also be beneficial in evaluating the chaotic oscillating dynamics of semiconductor lasers- not just for conventional phase stability analysis. In the present study, we demonstrated Allan variance analysis of the complex time series generated by a semiconductor laser with delayed feedback, including low-frequency fluctuations (LFFs), which exhibit both fast and slow dynamics. While the detection of LFFs is difficult with the conventional power spectrum analysis method in the low-frequency regime, the Allan variance approach clearly captured the appearance of multiple time-scale dynamics, such as LFFs. This study demonstrates that Allan variance can help in understanding and characterizing diverse laser dynamics, including LFFs, spanning a wide range of timescales.
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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.
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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.
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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 is estimated, but also a point spread function of turbulence from a single captured image by a reconstruction algorithm with CA support. The proposed method is demonstrated by simulation and an experiment in which point sources are recovered under severe turbulence.
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Optical phase conjugation is a known technique for optically reproducing an object behind a scattering medium. Here we present digital optical phase conjugation through scattering media with spatially and temporally incoherent light. This enables us to eliminate the inevitable light coherence and the need for interferometric measurement for optical phase conjugation. Moreover, we show a method for suppressing background noise, which is critical in incoherent optical phase conjugation. We numerically and experimentally demonstrate the proposed method with background suppression.
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This publisher's note contains a correction to Opt. Lett.47, 3844 (2022)10.1364/OL.464454.
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We present a method for computer-generated holography (CGH) using spatially and temporally incoherent light. The proposed method synthesizes a hologram cascade by solving an inverse problem for the propagation of incoherent light. The spatial incoherence removes speckle noise in CGH, and the temporal incoherence simplifies the optical setup, including the light source. We demonstrate two- and three-dimensional color image reproductions by a two-layer grayscale hologram cascade with a chip-on-board white light-emitting diode.