Quantum Reservoir Computing for Speckle-Disorder Potentials
| Autor: | Pere Mujal Torreblanca |
|---|---|
| Přispěvatelé: | Agencia Estatal de Investigación (España), Govern de les Illes Balears |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Speckle disorder
Quantum Physics Quantum reservoir computing MathematicsofComputing_NUMERICALANALYSIS FOS: Physical sciences quantum reservoir computing quantum machine learning information processing speckle disorder Disordered Systems and Neural Networks (cond-mat.dis-nn) Condensed Matter - Disordered Systems and Neural Networks Condensed Matter Physics Electronic Optical and Magnetic Materials Quantum Gases (cond-mat.quant-gas) Information processing Quantum Physics (quant-ph) Condensed Matter - Quantum Gases Quantum machine learning |
| Zdroj: | Condensed Matter; Volume 7; Issue 1; Pages: 17 Digital.CSIC. Repositorio Institucional del CSIC instname |
| Popis: | 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 shows to be enhanced when two-qubit correlations are employed. This research was funded by: the Spanish State Research Agency, through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D, grant number MDM-2017-0711; Comunitat Autònoma de les Illes Balears, Govern de les Illes Balears, through the QUAREC project, grant number PRD2018/47; and the Spanish State Research Agency through the QUARESC project, grant numbers PID2019-109094GB-C21 and -C22/AEI/10.13039/501100011033. |
| Databáze: | OpenAIRE |
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