001034121 001__ 1034121
001034121 005__ 20250203103442.0
001034121 037__ $$aFZJ-2024-06937
001034121 1001_ $$0P:(DE-Juel1)171686$$aAnsari, Mohammad$$b0$$ufzj
001034121 245__ $$aError mitigation of entangled states using brainbox quantum autoencoders
001034121 260__ $$c2023
001034121 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1734499981_22766
001034121 3367_ $$2ORCID$$aWORKING_PAPER
001034121 3367_ $$028$$2EndNote$$aElectronic Article
001034121 3367_ $$2DRIVER$$apreprint
001034121 3367_ $$2BibTeX$$aARTICLE
001034121 3367_ $$2DataCite$$aOutput Types/Working Paper
001034121 520__ $$aCurrent quantum hardware is subject to various sources of noise that limits the access to multi-qubit entangled states. Quantum autoencoder circuits with a single qubit bottleneck have showncapability to correct error in noisy entangled state. By introducing slightly more complex structuresin the bottleneck, the so-called brainboxes, the denoising process can take place faster and forstronger noise channels. Choosing the most suitable brainbox for the bottleneck is the result of atrade-off between noise intensity on the hardware, and the training impedance. Finally, by studyingRényi entropy flow throughout the networks we demonstrate that the localization of entanglementplays a central role in denoising through learning.
001034121 536__ $$0G:(DE-HGF)POF4-5224$$a5224 - Quantum Networking (POF4-522)$$cPOF4-522$$fPOF IV$$x0
001034121 536__ $$0G:(EU-Grant)743791$$aSUPERSPIN - Superconducting Spintronics for Highly Energery Efficient Cryogenic Memory Applications (743791)$$c743791$$fH2020-MSCA-IF-2016$$x1
001034121 7001_ $$0P:(DE-Juel1)188287$$aPazem, Josephine$$b1
001034121 909CO $$ooai:juser.fz-juelich.de:1034121$$pec_fundedresources$$pVDB$$popenaire
001034121 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171686$$aForschungszentrum Jülich$$b0$$kFZJ
001034121 9131_ $$0G:(DE-HGF)POF4-522$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5224$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Computing$$x0
001034121 9141_ $$y2024
001034121 920__ $$lyes
001034121 9201_ $$0I:(DE-Juel1)PGI-2-20110106$$kPGI-2$$lTheoretische Nanoelektronik$$x0
001034121 980__ $$apreprint
001034121 980__ $$aVDB
001034121 980__ $$aI:(DE-Juel1)PGI-2-20110106
001034121 980__ $$aUNRESTRICTED