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| 001 | 916699 | ||
| 005 | 20230123101906.0 | ||
| 037 | _ | _ | |a FZJ-2023-00039 |
| 100 | 1 | _ | |a Pazem, Josephine |0 P:(DE-Juel1)188287 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a APS March Meeting 2022 |c Chicago |d 2022-03-14 - 2022-03-18 |w USA |
| 245 | _ | _ | |a Beware of entropy phase transition! How to make quantum denoising successful? |
| 260 | _ | _ | |c 2022 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a Quantum autoencoders can help to generate denoised entanglement on a noisy neural network. However noise outweighs the process if it gets too strong. [1]We show that the flow of Rényi entropy in a subnet can perform as a measure whose phase transition determines denoising success or failure. Moreover, we rely on the study to implement a deformation of the network that results in improved tolerance against heavier noises.References:[1] D. Bondarenko and P. Feldmann, “Quantum autoencoders to denoise quantum data,” Phys. Rev. Lett., vol. 124, no. 13, p. 130502, 2020. |
| 536 | _ | _ | |a 5224 - Quantum Networking (POF4-522) |0 G:(DE-HGF)POF4-5224 |c POF4-522 |f POF IV |x 0 |
| 700 | 1 | _ | |a Ansari, Mohammad |0 P:(DE-Juel1)171686 |b 1 |u fzj |
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| 914 | 1 | _ | |y 2022 |
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| 980 | _ | _ | |a UNRESTRICTED |
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