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000907255 1001_ $$0P:(DE-Juel1)187565$$aContessi, Daniele$$b0$$eCorresponding author$$ufzj
000907255 245__ $$aDetection of Berezinskii-Kosterlitz-Thouless transition via Generative Adversarial Networks
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000907255 520__ $$aThe detection of phase transitions in quantum many-body systems with lowest possible prior knowledge of their details is among the most rousing goals of the flourishing application of machine-learning techniques to physical questions. Here, we train a Generative Adversarial Network (GAN) with the Entanglement Spectrum of a system bipartition, as extracted by means of Matrix Product States ans\"atze. We are able to identify gapless-to-gapped phase transitions in different one-dimensional models by looking at the machine inability to reconstruct outsider data with respect to the training set. We foresee that GAN-based methods will become instrumental in anomaly detection schemes applied to the determination of phase-diagrams.
000907255 536__ $$0G:(DE-HGF)POF4-5221$$a5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522)$$cPOF4-522$$fPOF IV$$x0
000907255 536__ $$0G:(EU-Grant)817482$$aPASQuanS - Programmable Atomic Large-Scale Quantum Simulation (817482)$$c817482$$fH2020-FETFLAG-2018-03$$x1
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000907255 7001_ $$0P:(DE-HGF)0$$aRicci, Elisa$$b1
000907255 7001_ $$0P:(DE-HGF)0$$aRecati, Alessio$$b2
000907255 7001_ $$0P:(DE-Juel1)177780$$aRizzi, Matteo$$b3
000907255 773__ $$0PERI:(DE-600)2886659-9$$a10.21468/SciPostPhys.12.3.107$$gVol. 12, no. 3, p. 107$$n3$$p107$$tSciPost physics$$v12$$x2542-4653$$y2022
000907255 8564_ $$uhttps://juser.fz-juelich.de/record/907255/files/SciPostPhys_12_3_107.pdf$$yOpenAccess
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000907255 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)187565$$a Dipartimento di Fisica, Università di Trento & INO-CNR BEC Center, 38123 Povo, Italy$$b0
000907255 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Dipartimento di Ingegneria e Scienza dell’Informazione, Università di Trento, & Deep Visual Learning research group, Fondazione Bruno Kessler (FBK), 38123 Povo, Italy$$b1
000907255 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Dipartimento di Fisica, Università di Trento & INO-CNR BEC Center, 38123 Povo, Italy$$b2
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000907255 9141_ $$y2022
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