Quantum Reservoir Computing for Speckle Disorder Potentials
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 str...
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| Tipo de recurso: | artículo |
| Estado: | Versión enviada para evaluación y publicación |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/271219 |
| Acceso en línea: | http://hdl.handle.net/10261/271219 |
| Access Level: | acceso abierto |
| Palabra clave: | Information processing Speckle disorder Quantum reservoir computing Quantum machine learning |
| Sumario: | 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. |
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