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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Condensed Matter |
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| Online Access: | https://www.mdpi.com/2410-3896/7/1/17 |
| _version_ | 1797472168966094848 |
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| author | Pere Mujal |
| author_facet | Pere Mujal |
| author_sort | Pere Mujal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:58:18Z |
| format | Article |
| id | doaj.art-b075fc25290c465aab38f71dbc763b51 |
| institution | Directory Open Access Journal |
| issn | 2410-3896 |
| language | English |
| last_indexed | 2024-03-09T19:58:18Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Condensed Matter |
| spelling | doaj.art-b075fc25290c465aab38f71dbc763b512023-11-24T00:50:35ZengMDPI AGCondensed Matter2410-38962022-01-01711710.3390/condmat7010017Quantum Reservoir Computing for Speckle Disorder PotentialsPere Mujal0IFISC, Institut de Física Interdisciplinària i Sistemes Complexos (UIB-CSIC), UIB Campus, E-07122 Palma de Mallorca, SpainQuantum 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.https://www.mdpi.com/2410-3896/7/1/17quantum reservoir computingquantum machine learninginformation processingspeckle disorder |
| spellingShingle | Pere Mujal Quantum Reservoir Computing for Speckle Disorder Potentials Condensed Matter quantum reservoir computing quantum machine learning information processing speckle disorder |
| title | Quantum Reservoir Computing for Speckle Disorder Potentials |
| title_full | Quantum Reservoir Computing for Speckle Disorder Potentials |
| title_fullStr | Quantum Reservoir Computing for Speckle Disorder Potentials |
| title_full_unstemmed | Quantum Reservoir Computing for Speckle Disorder Potentials |
| title_short | Quantum Reservoir Computing for Speckle Disorder Potentials |
| title_sort | quantum reservoir computing for speckle disorder potentials |
| topic | quantum reservoir computing quantum machine learning information processing speckle disorder |
| url | https://www.mdpi.com/2410-3896/7/1/17 |
| work_keys_str_mv | AT peremujal quantumreservoircomputingforspeckledisorderpotentials |