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| 001 | 907255 | ||
| 005 | 20230522125350.0 | ||
| 024 | 7 | _ | |a 10.21468/SciPostPhys.12.3.107 |2 doi |
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| 100 | 1 | _ | |a Contessi, Daniele |0 P:(DE-Juel1)187565 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Detection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks |
| 260 | _ | _ | |a Amsterdam |c 2022 |b SciPost Foundation |
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| 520 | _ | _ | |a The detection of phase transitions in quantum many-body systems with lowest possible prior knowledge of their details is among the most rousing goals of the flourishing application of machine-learning techniques to physical questions. Here, we train a Generative Adversarial Network (GAN) with the Entanglement Spectrum of a system bipartition, as extracted by means of Matrix Product States ans\"atze. We are able to identify gapless-to-gapped phase transitions in different one-dimensional models by looking at the machine inability to reconstruct outsider data with respect to the training set. We foresee that GAN-based methods will become instrumental in anomaly detection schemes applied to the determination of phase-diagrams. |
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| 536 | _ | _ | |a PASQuanS - Programmable Atomic Large-Scale Quantum Simulation (817482) |0 G:(EU-Grant)817482 |c 817482 |f H2020-FETFLAG-2018-03 |x 1 |
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| 700 | 1 | _ | |a Ricci, Elisa |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Recati, Alessio |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Rizzi, Matteo |0 P:(DE-Juel1)177780 |b 3 |
| 773 | _ | _ | |a 10.21468/SciPostPhys.12.3.107 |g Vol. 12, no. 3, p. 107 |0 PERI:(DE-600)2886659-9 |n 3 |p 107 |t SciPost physics |v 12 |y 2022 |x 2542-4653 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/907255/files/SciPostPhys_12_3_107.pdf |y OpenAccess |
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| 910 | 1 | _ | |a Dipartimento di Ingegneria e Scienza dell’Informazione, Università di Trento, & Deep Visual Learning research group, Fondazione Bruno Kessler (FBK), 38123 Povo, Italy |0 I:(DE-HGF)0 |b 1 |6 P:(DE-HGF)0 |
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