State estimation with quantum extreme learning machines beyond the scrambling time
Abstract Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quant...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Quantum Information |
| Online Access: | https://doi.org/10.1038/s41534-024-00927-5 |
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| _version_ | 1823861705314140160 |
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| author | Marco Vetrano Gabriele Lo Monaco Luca Innocenti Salvatore Lorenzo G. Massimo Palma |
| author_facet | Marco Vetrano Gabriele Lo Monaco Luca Innocenti Salvatore Lorenzo G. Massimo Palma |
| author_sort | Marco Vetrano |
| collection | DOAJ |
| description | Abstract Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quantifies how the spread of quantum information into correlations makes it irretrievable from local measurements. Here, we explore the tight relation between QIS and the predictive power of QELMs. In particular, we show efficient state estimation is possible even beyond the scrambling time, for many different types of dynamics — in fact, we show that in all the cases we studied, the reconstruction efficiency at long interaction times matches the optimal one offered by random global unitary dynamics. These results offer promising venues for robust experimental QELM-based state estimation protocols, as well as providing novel insights into the nature of QIS from a state estimation perspective. |
| format | Article |
| id | doaj-art-26c4d2acb8034a65bb624f022e5befd3 |
| institution | Kabale University |
| issn | 2056-6387 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Quantum Information |
| spelling | doaj-art-26c4d2acb8034a65bb624f022e5befd32025-02-09T12:49:02ZengNature Portfolionpj Quantum Information2056-63872025-02-011111810.1038/s41534-024-00927-5State estimation with quantum extreme learning machines beyond the scrambling timeMarco Vetrano0Gabriele Lo Monaco1Luca Innocenti2Salvatore Lorenzo3G. Massimo Palma4Università degli Studi di Palermo, Dipartimento di Fisica e Chimica—Emilio SegrèUniversità degli Studi di Palermo, Dipartimento di Fisica e Chimica—Emilio SegrèUniversità degli Studi di Palermo, Dipartimento di Fisica e Chimica—Emilio SegrèUniversità degli Studi di Palermo, Dipartimento di Fisica e Chimica—Emilio SegrèUniversità degli Studi di Palermo, Dipartimento di Fisica e Chimica—Emilio SegrèAbstract Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to efficiently process information encoded in input quantum states, avoiding the high computational cost of training more complicated nonlinear models. On the other hand, quantum information scrambling (QIS) quantifies how the spread of quantum information into correlations makes it irretrievable from local measurements. Here, we explore the tight relation between QIS and the predictive power of QELMs. In particular, we show efficient state estimation is possible even beyond the scrambling time, for many different types of dynamics — in fact, we show that in all the cases we studied, the reconstruction efficiency at long interaction times matches the optimal one offered by random global unitary dynamics. These results offer promising venues for robust experimental QELM-based state estimation protocols, as well as providing novel insights into the nature of QIS from a state estimation perspective.https://doi.org/10.1038/s41534-024-00927-5 |
| spellingShingle | Marco Vetrano Gabriele Lo Monaco Luca Innocenti Salvatore Lorenzo G. Massimo Palma State estimation with quantum extreme learning machines beyond the scrambling time npj Quantum Information |
| title | State estimation with quantum extreme learning machines beyond the scrambling time |
| title_full | State estimation with quantum extreme learning machines beyond the scrambling time |
| title_fullStr | State estimation with quantum extreme learning machines beyond the scrambling time |
| title_full_unstemmed | State estimation with quantum extreme learning machines beyond the scrambling time |
| title_short | State estimation with quantum extreme learning machines beyond the scrambling time |
| title_sort | state estimation with quantum extreme learning machines beyond the scrambling time |
| url | https://doi.org/10.1038/s41534-024-00927-5 |
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