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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.09079 |
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| _version_ | 1866916240708075520 |
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| author | Sgroi, S. Zicari, G. Imparato, A. Paternostro, M. |
| author_facet | Sgroi, S. Zicari, G. Imparato, A. Paternostro, M. |
| contents | We study the excitation transfer across a fully connected quantum network whose sites energies can be artificially designed. Starting from a simplified model of a broadly-studied physical system, we systematically optimize its local energies to achieve high excitation transfer for various environmental conditions, using an adaptive Gradient Descent technique and Automatic Differentiation. We show that almost perfect transfer can be achieved with and without local dephasing, provided that the dephasing rates are not too large. We investigate our solutions in terms of resilience against variations in either the network connection strengths, or size, as well as coherence losses. We highlight the different features of a dephasing-free and dephasing-driven transfer. Our work gives further insight into the interplay between coherence and dephasing effects in excitation-transfer phenomena across fully connected quantum networks. In turn, this will help designing optimal transfer in artificial open networks through the simple manipulation of local energies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_09079 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Efficient excitation-transfer across fully connected networks via local-energy optimization Sgroi, S. Zicari, G. Imparato, A. Paternostro, M. Quantum Physics We study the excitation transfer across a fully connected quantum network whose sites energies can be artificially designed. Starting from a simplified model of a broadly-studied physical system, we systematically optimize its local energies to achieve high excitation transfer for various environmental conditions, using an adaptive Gradient Descent technique and Automatic Differentiation. We show that almost perfect transfer can be achieved with and without local dephasing, provided that the dephasing rates are not too large. We investigate our solutions in terms of resilience against variations in either the network connection strengths, or size, as well as coherence losses. We highlight the different features of a dephasing-free and dephasing-driven transfer. Our work gives further insight into the interplay between coherence and dephasing effects in excitation-transfer phenomena across fully connected quantum networks. In turn, this will help designing optimal transfer in artificial open networks through the simple manipulation of local energies. |
| title | Efficient excitation-transfer across fully connected networks via local-energy optimization |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2211.09079 |