Quantum state-agnostic work extraction (almost) without dissipation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912376064835584 |
|---|---|
| author | Lumbreras, Josep Huang, Ruo Cheng Hu, Yanglin Gu, Mile Tomamichel, Marco |
| author_facet | Lumbreras, Josep Huang, Ruo Cheng Hu, Yanglin Gu, Mile Tomamichel, Marco |
| contents | We investigate work extraction protocols designed to transfer the maximum possible energy to a battery using sequential access to $N$ copies of an unknown pure qubit state. The core challenge is designing interactions to optimally balance two competing goals: charging of the battery optimally using the qubit in hand, and acquiring more information by qubit to improve energy harvesting in subsequent rounds. Here, we leverage exploration-exploitation trade-off in reinforcement learning to develop adaptive strategies achieving energy dissipation that scales only poly-logarithmically in $N$. This represents an exponential improvement over current protocols based on full state tomography. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09456 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum state-agnostic work extraction (almost) without dissipation Lumbreras, Josep Huang, Ruo Cheng Hu, Yanglin Gu, Mile Tomamichel, Marco Quantum Physics Artificial Intelligence Machine Learning We investigate work extraction protocols designed to transfer the maximum possible energy to a battery using sequential access to $N$ copies of an unknown pure qubit state. The core challenge is designing interactions to optimally balance two competing goals: charging of the battery optimally using the qubit in hand, and acquiring more information by qubit to improve energy harvesting in subsequent rounds. Here, we leverage exploration-exploitation trade-off in reinforcement learning to develop adaptive strategies achieving energy dissipation that scales only poly-logarithmically in $N$. This represents an exponential improvement over current protocols based on full state tomography. |
| title | Quantum state-agnostic work extraction (almost) without dissipation |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.09456 |