Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Lina Jaurigue"'
Publikováno v:
Journal of High Energy Physics, Vol 2024, Iss 10, Pp 1-21 (2024)
Abstract 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 Hamiltonian to construct a basis for the Krylov state space and the co
Externí odkaz:
https://doaj.org/article/0faaccae3b3e4a4fbf2c7a4a74a53f03
Autor:
Lina Jaurigue
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035058 (2024)
Forecasting timeseries based upon measured data is needed in a wide range of applications and has been the subject of extensive research. A particularly challenging task is the forecasting of timeseries generated by chaotic dynamics. In recent years
Externí odkaz:
https://doaj.org/article/74d52c0bf12e4d3599bc6192f48ac801
Publikováno v:
Physical Review Research, Vol 6, Iss 1, p 013051 (2024)
We propose a scheme that can enhance the performance and reduce the computational cost of quantum reservoir computing. Quantum reservoir computing is a computing approach which aims at utilizing the complexity and high dimensionality of small quantum
Externí odkaz:
https://doaj.org/article/ad72ffb0f5364a3caa7d07f1cd1e21fa
Autor:
Lina Jaurigue, Kathy Lüdge
Publikováno v:
Neuromorphic Computing and Engineering, Vol 4, Iss 1, p 014001 (2024)
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 directly including externally controllable task specific timescales on th
Externí odkaz:
https://doaj.org/article/3583bad4b8c243e18f9d0a024f1d5518
Autor:
Lina Jaurigue, Kathy Lüdge
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-3 (2022)
Standfirst 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
Externí odkaz:
https://doaj.org/article/fcc622d46d75426889c64c7a3621a113
Publikováno v:
Entropy, Vol 23, Iss 12, p 1560 (2021)
Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is parti
Externí odkaz:
https://doaj.org/article/bdcd2e9ca34a4e698bdf8ecd7ce3fea0
Publikováno v:
Nanophotonics. 12:937-947
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 o
Publikováno v:
IEEE Journal of Selected Topics in Quantum Electronics. 26:1-8
Reservoir computing is a neuromorphic computing scheme inspired by the human brain. It has found great success as a versatile hardware-compatible application of machine learning concepts. In this paper, we highlight the fundamental working principles
Publikováno v:
Entropy; Volume 23; Issue 12; Pages: 1560
Entropy, Vol 23, Iss 1560, p 1560 (2021)
Entropy
Entropy, Vol 23, Iss 1560, p 1560 (2021)
Entropy
Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this meth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bb5cf5fb814e7d4f1639b4761e6f193
https://doi.org/10.3390/e23121560
https://doi.org/10.3390/e23121560
Autor:
Dmitry Kazakov, Lina Jaurigue, Andreas Klehr, Christoph Weber, Stefan Breuer, Federico Capasso, Marcus Ossiander, Andrea Knigge, Kathy Lüdge, Stefan Meinecke, Dominik Auth
Publikováno v:
Novel In-Plane Semiconductor Lasers XX.
Optical self-injection stabilization of a passively mode-locked InGaAs double quantum well semiconductor laser is reported. Time-domain, frequency-domain and spectral domain measurement results in dependence on feedback strength and feedback delay de