Berlin 2024 – wissenschaftliches Programm

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QI: Fachverband Quanteninformation

QI 18: Poster II

QI 18.15: Poster

Mittwoch, 20. März 2024, 11:00–14:30, Poster A

Quantum Reservoir Computing in coupled cavities with weak measurements — •Lara Anna Giebeler, Niclas Götting, Frederik Lohof, and Christopher Gies — Institute for Theoretical Physics, University of Bremen, 28359 Bremen, Germany

Quantum Reservoir Computing (QRC) has emerged as a new quantum machine learning approach to process time-series information by utilizing the coherent dynamics inherent to quantum systems. A possible experimental realization of QRC could be achieved by using semiconductor quantum dots embedded in coupled-cavitiy-arrays as a reservoir.

Early implementations for QRC propose the use of projective (strong) measurements, but these projective measurements induce a back-action on the system, disturbing the dynamics of the reservoir and prohibiting online time-series processing. In the context of actual physical implementations, it has been proposed to use weak measurements [1]. Although these weak measurements provide only reduced information about the system, they also result in little back-action on the reservoir and retain its properties. This work considers the usage of these weak measurements in coupled-cavity QRC to enable online time-series processing.

[1] Mujal, P., Martínez-Peña, R., Giorgi, G.L. et al. npj Quantum Inf 9, 16 (2023). https://doi.org/10.1038/s41534-023-00682-z

Keywords: Reservoir Computing; Weak Measurements; Cavity QED; Quantum Machine Learning