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• W4210436764 abstract "Quantum reservoir computing is a machine learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided by the reservoir combined with a simple and fast training strategy. In this work, this technique is introduced with a quantum reservoir of spins and it is applied to find the ground state energy of an additional quantum system. The quantum reservoir computer is trained with a linear model to predict the lowest energy of a particle in the presence of different speckle disorder potentials. The performance of the task is analyzed with a focus on the observable quantities extracted from the reservoir and it is shown to be enhanced when two-qubit correlations are employed." @default.
• W4210436764 created "2022-02-08" @default.
• W4210436764 creator A5046854072 @default.
• W4210436764 date "2022-01-28" @default.
• W4210436764 modified "2023-10-14" @default.
• W4210436764 title "Quantum Reservoir Computing for Speckle Disorder Potentials" @default.
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• W4210436764 doi "https://doi.org/10.3390/condmat7010017" @default.
• W4210436764 hasPublicationYear "2022" @default.
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