000916698 001__ 916698
000916698 005__ 20230123101906.0
000916698 037__ $$aFZJ-2023-00038
000916698 1001_ $$0P:(DE-Juel1)188287$$aPazem, Josephine$$b0$$eCorresponding author$$ufzj
000916698 1112_ $$aAPS Meeting 2022$$cChicago$$d2022-03-14 - 2022-03-18$$wUSA
000916698 245__ $$aImproving the resilience of quantum denoising process
000916698 260__ $$c2022
000916698 3367_ $$033$$2EndNote$$aConference Paper
000916698 3367_ $$2DataCite$$aOther
000916698 3367_ $$2BibTeX$$aINPROCEEDINGS
000916698 3367_ $$2DRIVER$$aconferenceObject
000916698 3367_ $$2ORCID$$aLECTURE_SPEECH
000916698 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1672809829_25435$$xInvited
000916698 520__ $$aQuantum autoencoders aim to automate denoising algorithms. These quantum neural networks are trained to surpass noise channels and return arbitrary entangled states of our interest with high-fidelity. So far the successful training has shown tolerance up to 30% of bit flip and depolarization. Stronger noise results in poor training and denoising failure. [1]In this talk I describe an inexpensive change in the network topology that can be extendable to all scales and can improve the tolerance significantly.  This has a side advantage that it can provide even higher fidelity values for successful training. It indeed helps the encoder by reducing the dimension of the decision boundary between perfect and noisy states. Such a simplification of the classification task relies heavily on quantum properties of the neural units. We show that Renyi entropy associated with a small partition of the network undergoes a second order phase transition when training fails, and this can serve as a good measure to distinguish between failure and success in denoising process. [1] D. Bondarenko and P. Feldmann, “Quantum autoencoders to denoise quantum data”, Phys. Rev. Lett., vol. 124, no. 13, p. 130502, 2020.
000916698 536__ $$0G:(DE-HGF)POF4-5224$$a5224 - Quantum Networking (POF4-522)$$cPOF4-522$$fPOF IV$$x0
000916698 7001_ $$0P:(DE-Juel1)171686$$aAnsari, Mohammad$$b1$$ufzj
000916698 909CO $$ooai:juser.fz-juelich.de:916698$$pVDB
000916698 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188287$$aForschungszentrum Jülich$$b0$$kFZJ
000916698 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)171686$$aForschungszentrum Jülich$$b1$$kFZJ
000916698 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$$x0
000916698 9141_ $$y2022
000916698 920__ $$lyes
000916698 9201_ $$0I:(DE-Juel1)PGI-2-20110106$$kPGI-2$$lTheoretische Nanoelektronik$$x0
000916698 980__ $$aconf
000916698 980__ $$aVDB
000916698 980__ $$aI:(DE-Juel1)PGI-2-20110106
000916698 980__ $$aUNRESTRICTED