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| 001 | 941218 | ||
| 005 | 20240625095032.0 | ||
| 020 | _ | _ | |a 978-3-95806-641-0 |
| 024 | 7 | _ | |2 Handle |a 2128/33794 |
| 024 | 7 | _ | |2 URN |a urn:nbn:de:0001-2023013111 |
| 037 | _ | _ | |a FZJ-2023-00822 |
| 100 | 1 | _ | |0 P:(DE-Juel1)188287 |a Pazem, Josephine |b 0 |e Corresponding author |
| 245 | _ | _ | |a Denoising with Quantum Machine Learning |f - 2022-11-24 |
| 260 | _ | _ | |a Jülich |b Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag |c 2022 |
| 300 | _ | _ | |a 106 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)3 |2 PUB:(DE-HGF) |a Book |m book |
| 336 | 7 | _ | |2 DataCite |a Output Types/Supervised Student Publication |
| 336 | 7 | _ | |0 2 |2 EndNote |a Thesis |
| 336 | 7 | _ | |2 BibTeX |a MASTERSTHESIS |
| 336 | 7 | _ | |2 DRIVER |a masterThesis |
| 336 | 7 | _ | |0 PUB:(DE-HGF)19 |2 PUB:(DE-HGF) |a Master Thesis |b master |m master |s 1674810196_23400 |
| 336 | 7 | _ | |2 ORCID |a SUPERVISED_STUDENT_PUBLICATION |
| 490 | 0 | _ | |a Schriften des Forschungszentrums Jülich Reihe Information / Information |v 82 |
| 502 | _ | _ | |a Masterarbeit, RWTH Aachen University, 2022 |b Masterarbeit |c RWTH Aachen University |d 2022 |
| 520 | _ | _ | |a This master thesis explores aspects of quantum machine learning in the light of an application to dampen the effects of noise on NISQ processors. We investigate the possibility of designing machine learning models that can be accommodated entirely on quantum devices without the help of classical computers. With Dissipative Quantum Neural Networks, we simulate a quantum feed-forward neural network for denoising: the Quantum Autoencoder. We assign it to correct bit-flip noise in states that can exist only quantum mechanically, namely the highly entangled GHZ-states. The numerical simulations report that the QAE can recover the target states up to some tolerance threshold on the noise intensity. To understand the limitations, we investigate the mechanisms behind the completion of the denoising task with quantum entropy measures. The observationsreveal that the latent representation is key to reconstructing the desired state in the outputs. Consequently, we propose an inexpensive modification of the original QAE: the brain box-enhanced QAE. The addition of complexity in the intermediate layers of the network maximizes the robustness of the QAE in a setting where only a finite-size training data set is available. We close the argument with a discussion on the generalization properties of the network. |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-5224 |a 5224 - Quantum Networking (POF4-522) |c POF4-522 |f POF IV |x 0 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/941218/files/Information_82.pdf |y OpenAccess |
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| 910 | 1 | _ | |0 I:(DE-588b)5008462-8 |6 P:(DE-Juel1)188287 |a Forschungszentrum Jülich |b 0 |k FZJ |
| 913 | 1 | _ | |0 G:(DE-HGF)POF4-522 |1 G:(DE-HGF)POF4-520 |2 G:(DE-HGF)POF4-500 |3 G:(DE-HGF)POF4 |4 G:(DE-HGF)POF |9 G:(DE-HGF)POF4-5224 |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |v Quantum Computing |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0510 |2 StatID |a OpenAccess |
| 915 | _ | _ | |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |a Creative Commons Attribution CC BY 4.0 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)PGI-2-20110106 |k PGI-2 |l Theoretische Nanoelektronik |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-3-20090406 |k IAS-3 |l Theoretische Nanoelektronik |x 1 |
| 980 | _ | _ | |a master |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a book |
| 980 | _ | _ | |a I:(DE-Juel1)PGI-2-20110106 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-3-20090406 |
| 980 | 1 | _ | |a FullTexts |
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