128 citations · 632 across the 38 of their papers we have counts for
38 papers
Optically Writable Atomic Vapor Memory as a Substrate for Optical Reservoir Computing
Elizabeth Robertson, Mingwei Yang, Lina Jaurigue +3
We present an optical random access memory (ORAM) based on warm cesium (Cs) atomic vapor and demonstrate its operation as the physical substrate of a reservoir computer. Informatio…
Multistability and state-switching in series-coupled resonant tunneling diodes
Jannis Waldmann, Jonnel Jaurigue, Kathy Lüdge
Resonant tunneling diodes (RTDs) embedded in an electrical circuit are known for their neuron-like response characteristics, which makes them promising candidates for neuromorphic…
Reservoir Computing with a single Josephson junction
George Baxevanis, Kathy Lüdge, Johanne Hizanidis
Physical reservoir computing exploits the nonlinear dynamics of a physical system to perform information processing tasks. Josephson junctions (JJs), as nonlinear superconducting d…
Distributed Coherent Optical Computing via Injection-Locked Photonic Networks
Shenghan Gao, Kathy Lüdge, Francesco Da Ros +1
Coherent photonic computing uses both the phase and amplitude of light to implement linear operations such as dot products and matrix multiplication but requires phase stability be…
Memory-Nonlinearity Trade-off across Quantum Reservoir Computing Frameworks
Saud Čindrak, Lara Giebeler, Niclas Götting +2
Quantum reservoir computing (QRC) harnesses driven quantum dynamics for time-series processing, yet the mechanisms behind the differing performance levels across its many implement…
On Minimizing Krylov Complexity Using Higher-Order Generators
Saud Čindrak, Kathy Lüdge
Krylov complexity provides a powerful framework for characterizing the dynamical evolution of quantum systems through the spreading of states in Krylov space. The motivation for th…
Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine
Mio Kawanabe, Saud Cindrak, Kathy Ludge +3
We proposed a time-delayed quantum extreme learning machine (TD-QELM) for efficient time-series prediction on noisy intermediate-scale quantum (NISQ) devices. By encoding multiple…
Data-Driven Performance Measures using Global Properties of Attractors for Black-Box Surrogate Models of Chaotic Systems
Luci Fumagalli, Kathy Lüdge, Jana de Wiljes +2
In climate systems, physiological models, optics, and many more, surrogate models are developed to reconstruct chaotic dynamical systems. We introduce four data-driven measures usi…
From Krylov Complexity to Observability: Capturing Phase Space Dimension with Applications in Quantum Reservoir Computing
Saud Čindrak, Kathy Lüdge, Lina Jaurigue
We demonstrate that time-evolved operators can construct a Krylov space to compute Operator complexity and introduce Krylov observability as a measure of effective phase space dime…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
High-Speed Time Series Prediction with a GHz-rate Photonic Spiking Neural Network built with a single VCSEL
Dafydd Owen-Newns, Lina Jaurigue, Josh Robertson +4
Photonic technologies hold significant potential for creating innovative, high-speed, efficient and hardware-friendly neuromorphic computing platforms. Neuromorphic photonic method…
Time-multiplexed Reservoir Computing with Quantum-Dot Lasers: Does more complexity lead to better performance?
Huifang Dong, Lina Jaurigue, Kathy Lüdge
Reservoir computing with optical devices offers an energy-efficient approach for time-series forecasting. Quantum dot lasers with feedback are modelled in this paper to explore the…
Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
Saud Čindrak, Lina Jaurigue, Kathy Lüdge
This study employs Krylov-based information measures to understand task performance in quantum reservoir computing, a sub-field of quantum machine learning. In our study we show th…
Canard cascading in networks with adaptive mean-field coupling
Juan Balzer, Rico Berner, Kathy Lüdge +3
Canard cascading (CC) is observed in dynamical networks with global adaptive coupling. It is a fast-slow phenomenon characterized by a recurrent sequence of fast transitions betwee…
Measurable Krylov Spaces and Eigenenergy Count in Quantum State Dynamics
Saud Čindrak, Adrian Paschke, Lina Jaurigue +1
In this work, we propose a quantum-mechanically measurable basis for the computation of spread complexity. Current literature focuses on computing different powers of the Hamiltoni…
Data-Driven Acceleration of Multi-Physics Simulations
Stefan Meinecke, Malte Selig, Felix Köster +2
Multi-physics simulations play a crucial role in understanding complex systems. However, their computational demands are often prohibitive due to high dimensionality and complex in…
Stability of Optically Injected Two-State Quantum-Dot Lasers
Stefan Meinecke, Benjamin Lingnau, André Röhm +1
Simultaneous two-state lasing is a unique property of semiconductor quantum-dot (QD) lasers. This not only changes steady-state characteristics of the laser device but also its dyn…
Efficient timing jitter simulation for passively mode-locked semiconductor lasers
Stefan Meinecke, Kathy Lüdge
Efficient simulation of the timing jitter in passively mode-locking lasers is key to their numerical investigation and optimization. We introduce a method based on the pulse-period…
Optimizing the Cavity-Arm Ratio of V-Shaped Semiconductor Disk Lasers
Stefan Meinecke, Kathy Lüdge
Passively mode-locked semiconductor disk lasers have received tremendous attention from both science and industry. Their relatively inexpensive production combined with excellent p…
Data-Driven Forecasting of Non-Equilibrium Solid-State Dynamics
Stefan Meinecke, Felix Köster, Dominik Christiansen +3
We present a data-driven approach to efficiently approximate nonlinear transient dynamics in solid-state systems. Our proposed machine-learning model combines a dimensionality redu…
Efficient Optimisation of Physical Reservoir Computers using only a Delayed Input
Enrico Picco, Lina Jaurigue, Kathy Lüdge +1
We present an experimental validation of a recently proposed optimization technique for reservoir computing, using an optoelectronic setup. Reservoir computing is a robust framewor…
Reducing hyperparameter dependence by external timescale tailoring
Lina C. Jaurigue, Kathy Lüdge
Task specific hyperparameter tuning in reservoir computing is an open issue, and is of particular relevance for hardware implemented reservoirs. We investigate the influence of dir…
Solving the time-complexity problem and tuning the performance of quantum reservoir computing by artificial memory restriction
Saud Čindrak, Brecht Donvil, Kathy Lüdge +1
Quantum reservoir computing is a computing approach which aims at utilising the complexity and high-dimensionality of small quantum systems, together with the fast trainability of…
Multiplexed random-access optical memory in warm cesium vapor
Leon Meßner, Elizabeth Robertson, Luisa Esguerra +2
The ability to store large amounts of photonic quantum states is regarded as substantial for future optical quantum computation and communication technologies. However, research fo…