SKM 2023 – wissenschaftliches Programm
DY 9.2: Vortrag
Montag, 27. März 2023, 14:15–14:30, MOL 213
Harnessing the exponential Hilbert space dimension of quantum systems for reservoir computing — •Niclas Götting1,2, Frederik Lohof1,2, and Christopher Gies1,2 — 1Institute for Theoretical Physics, University of Bremen, Bremen — 2Bremen Center for Computational Material Science, University of Bremen, Bremen
With the ever growing prevalence of machine learning in science and industry, the machine learning paradigm of reservoir computing has gained new attention. While classical reservoir computers have proven to be able to solve various prediction tasks by exploiting the complex dynamics of classical systems, their quantum counterparts are yet to be fully explored.
Coherent quantum systems exhibit properties like superposition and quantum entanglement, which in principle lead to an exponential scaling of the reservoir phase space dimension with respect to the number of quantum particles. The question arises if these quantum reservoir computers (QRCs) can energy and space efficiently outperform their classical analogues in complex prediction tasks.
As a first step we investigate the transverse-field Ising model as a QRC to find the link between the properties of the quantum network, the phase space dimension, and the performance of the QRC.