Data-driven forecasting of nonequilibrium solid-state dynamics. - In: Physical review, ISSN 1550-235X, Bd. 107 (2023), 18, S. 184306-1-184306-18
We present a data-driven approach to efficiently approximate nonlinear transient dynamics in solid-state systems. Our proposed machine-learning model combines a dimensionality reduction stage with a nonlinear vector autoregression scheme. We report an outstanding time-series forecasting performance combined with an easy-to-deploy model and an inexpensive training routine. Our results are of great relevance as they have the potential to massively accelerate multiphysics simulation software and thereby guide the future development of solid-state-based technologies.
https://doi.org/10.1103/PhysRevB.107.184306
Spontaneous emission noise resilience of coupled nanolasers. - In: Frontiers in photonics, ISSN 2673-6853, Bd. 4 (2023), 1169988, S. 01-06
We investigate the spontaneous emission noise resilience of the phase-locked operation of two delay-coupled nanolasers. The system is modeled by semi-classical Maxwell-Bloch rate equations with stochastic Langevin-type noise sources. Our results reveal that a polarization dephasing time of two to three times the cavity photon lifetime maximizes the system’s ability to remain phase-locked in the presence of noise-induced perturbations. The Langevin noise term is caused by spontaneous emission processes which change both the intensity auto-correlation properties of the solitary lasers and the coupled system. In an experimental setup, these quantities are measurable and can be directly compared to our numerical data. The strong parameter dependence of the noise tolerance that we find may show possible routes for the design of robust on-chip integrated networks of nanolasers.
https://doi.org/10.3389/fphot.2023.1169988
Multiplexed random-access optical memory in warm cesium vapor. - In: Optics express, ISSN 1094-4087, Bd. 31 (2023), 6, S. 10150-10158
The ability to store large amounts of photonic quantum states is regarded as substantial for future optical quantum computation and communication technologies. However, research for multiplexed quantum memories has been focused on systems that show good performance only after an elaborate preparation of the storage media. This makes it generally more difficult to apply outside a laboratory environment. In this work, we demonstrate a multiplexed random-access memory to store up to four optical pulses using electromagnetically induced transparency in warm cesium vapor. Using a Λ-System on the hyperfine transitions of the Cs D1 line, we achieve a mean internal storage efficiency of 36% and a 1/e lifetime of 3.2 µs. In combination with future improvements, this work facilitates the implementation of multiplexed memories in future quantum communication and computation infrastructures.
https://doi.org/10.1364/OE.483642
Deriving task specific performance from the information processing capacity of a reservoir computer. - In: Nanophotonics, ISSN 2192-8614, Bd. 12 (2023), 5, S. 937-947
In the reservoir computing literature, the information processing capacity is frequently used to characterize the computing capabilities of a reservoir. However, it remains unclear how the information processing capacity connects to the performance on specific tasks. We demonstrate on a set of standard benchmark tasks that the total information processing capacity correlates poorly with task specific performance. Further, we derive an expression for the normalized mean square error of a task as a weighted function of the individual information processing capacities. Mathematically, the derivation requires the task to have the same input distribution as used to calculate the information processing capacities. We test our method on a range of tasks that violate this requirement and find good qualitative agreement between the predicted and the actual errors as long as the task input sequences do not have long autocorrelation times. Our method offers deeper insight into the principles governing reservoir computing performance. It also increases the utility of the evaluation of information processing capacities, which are typically defined on i.i.d. input, even if specific tasks deliver inputs stemming from different distributions. Moreover, it offers the possibility of reducing the experimental cost of optimizing physical reservoirs, such as those implemented in photonic systems.
https://doi.org/10.1515/nanoph-2022-0415
Photonic reservoir computing with non-linear memory cells: interplay between topology, delay and delayed input. - In: Emerging Topics in Artificial Intelligence (ETAI) 2022, (2022), S. 1220408-1-1220408-7
Photonic reservoir computing is an emerging topic due to the possibility to realize very fast devices with minimal training effort. We will discuss the reservoir computing performance of memory cells with a focus on the impact of delay lines and the interplay between coupling topology and performance for various benchmark tasks. We will further show that additional delayed input can be beneficial for reservoir computing setups in general, as it provides an easy tuning parameter, which can improve the performance of a reservoir on a range of tasks.
https://doi.org/10.1117/12.2633339
Optimizing the cavity-arm ratio of V-shaped semiconductor disk lasers. - In: Physical review applied, ISSN 2331-7019, Bd. 18 (2022), 6, S. 064070
Passively mode-locked semiconductor disk lasers have received tremendous attention from both science and industry. Their relatively inexpensive production combined with excellent pulse performance and great emission-wavelength flexibility make them suitable laser candidates for applications ranging from frequency-comb tomography to spectroscopy. However, due to the interaction of the active medium dynamics and the device geometry, emission instabilities occur at high pump powers and thereby limit their performance potential. Hence, understanding those instabilities becomes critical for an optimal laser design. Using a delay-differential equation model, we are able to detect, understand, and classify three distinct instabilities that limit the maximum achievable pump power for the fundamental mode-locking state and link them to characteristic positive-net-gain windows. We furthermore derive a simple analytic approximation in order to quantitatively describe the stability boundary. Our results enable us to predict the optimal laser-cavity configuration with respect to positive-net-gain instabilities and therefore may be of great relevance for the future development of passively mode-locking semiconductor disk lasers.
https://doi.org/10.1103/PhysRevApplied.18.064070
Master memory function for delay-based reservoir computers with single-variable dynamics. - In: IEEE transactions on neural networks and learning systems, ISSN 2162-237X, Bd. 0 (2022), 0, S. 1-14
We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity for any delay-based single-variable reservoir with small inputs. Moreover, we propose an analytical description of the MMF that enables its efficient and fast computation. Our approach can be applied not only to single-variable delay-based reservoirs governed by known dynamical rules, such as the Mackey-Glass or Stuart-Landau-like systems, but also to reservoirs whose dynamical model is not available.
https://doi.org/10.1109/TNNLS.2022.3220532
Role of delay-times in delay-based photonic reservoir computing. - In: Optical materials express, ISSN 2159-3930, Bd. 12 (2022), 3, S. 1214-1231
Delay-based reservoir computing has gained a lot of attention due to the relative simplicity with which this concept can be implemented in hardware. However, unnecessary constraints are commonly placed on the relationship between the delay-time and the input clock-cycle, which can have a detrimental effect on the performance. We review the existing literature on this subject and introduce the concept of delay-based reservoir computing in a manner that demonstrates that no predefined relationship between the delay-time and the input clock-cycle is required for this computing concept to work. Choosing the delay-times independent of the input clock-cycle, one gains an important degree of freedom. Consequently, we discuss ways to improve the computing performance of a reservoir formed by delay-coupled oscillators and show the impact of delay-time tuning in such systems.
https://doi.org/10.1364/OME.451016
Connecting reservoir computing with statistical forecasting and deep neural networks. - In: Nature Communications, ISSN 2041-1723, Bd. 13 (2022), 227, S. 1-3
Among the existing machine learning frameworks, reservoir computing demonstrates fast and low-cost training, and its suitability for implementation in various physical systems. This Comment reports on how aspects of reservoir computing can be applied to classical forecasting methods to accelerate the learning process, and highlights a new approach that makes the hardware implementation of traditional machine learning algorithms practicable in electronic and photonic systems.
https://doi.org/10.1038/s41467-021-27715-5
Three-dimensional optical microcavities: from geometric phases to tailored far-field emission. - Ilmenau : Universitätsbibliothek, 2021. - 1 Online-Ressource (iii, 204 Seiten)
Technische Universität Ilmenau, Dissertation 2021
Diese Arbeit behandelt dreidimensionale optische Mikrokavitäten in Bezug auf ihre Resonanzmoden. Die optischen Mikrokavitäten reichen dabei von Möbiusband-Kavitäten über zylindrische und kegelförmige Ringkavitäten sowie kegelförmige Tube-Kavitäten bis hin zu Arrays von Lima¸con-Kavitäten. Im ersten Teil werden flüstergalerieartige Moden von dielektrischen Möbiusband-Kavitäten mit Hilfe von FDTD-Simulationen untersucht. Die Topologie des Möbiusbands erlaubt die Entstehung einer geometrischen Phase und zwar der Pancharatnam-Phase. Darauf aufbauend wird untersucht, wie die Pancharatnam-Phase durch Verkürzung der Länge des verdrehten Anteils oder durch Erhöhung der Dicke des Möbiusbands manipuliert werden kann. Dabei untersuchen wir, wie die Polarisation und die Fernfelder der flüstergalerieartigen Moden beeinflusst werden. Außerdem wird die Nonagon-Möbiusband-Kavität - eine Möbiusband-Kavität mit Querschnittsform dreifacher Rotationssymmetrie - eingeführt, die ebenfalls eine Manipulation der Pancharatnam-Phase ermöglicht. Im zweiten Teil werden propagierende flüstergalerieartige Moden in zylindrischen Ringkavitäten, konischen Ringkavitäten und konischen Tube-Kavitäten mittels FDTD-Simulationen und vektorieller Beugungstheorie untersucht. Der propagierende Charakter der Moden ermöglicht die sogenannte Spin-Richtungs-Wechselwirkung des Lichts. Darauf aufbauend wird untersucht, wie die Fernfeldpolarisation durch die axiale Morphologie der flüstergalerieartigen Moden und durch geometrische Eigenschaften der Kavitäten wie die Öffnungswinkel von konischen Ring- und Tube-Kavitäten beeinflusst wird. Mit Hilfe vektorieller Beugungstheorie wird ein qualitativer Zusammenhang zwischen den lokalen Eigenschaften der Moden im Inneren der Kavität und der Fernfeldpolarisation beschrieben. Dabei wird die Rolle von Beugung und Präzession des elektrischen Feldvektors um die Kavitätenachse diskutiert. Es wird gezeigt, dass elliptische und zirkulare Polarisationszustände im Fernfeld unmittelbar durch propagierende flüstergalerieartige Moden auftreten, auch ohne inhomogenes oder anisotropes Kavitätenmaterial. Im dritten Teil wird die Fernfeldabstrahlung von linearen Arrays bestehend aus Lima¸con-Kavitäten mithilfe von FDTD-Simulationen untersucht. Während das Fernfeld einer einzelnen Lima¸con-Kavität gerichtete Emission aufweist, wird untersucht, wie sich diese gerichtete Emission in Abhängigkeit der Arrayeigenschaften wie dem Abstand zwischen den Kavitäten und der Anzahl der Kavitäten ändert. Es wird gezeigt, dass die Abstrahlung des Arrays entweder weiter verstärkt (Superdirektionalität) oder sogar umgekehrt Kavitäten werden kann (Richtungsumkehr).
https://nbn-resolving.org/urn:nbn:de:gbv:ilm1-2021000314