Engineering quantum reservoirs through Krylov complexity, expressivity, and observability
This study employs Krylov-based information measures to understand task performance in quantum reservoir computing, a subfield of quantum machine learning. In our study, we show that fidelity and spread complexity can only explain the task performance for short time evolutions of the quantum systems...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
American Physical Society
2025-11-01
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| Serie: | Physical Review Research |
| Accesso online: | http://doi.org/10.1103/84f3-63mz |
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