001038566 001__ 1038566
001038566 005__ 20250131215342.0
001038566 0247_ $$2arXiv$$aarXiv:2403.07462
001038566 037__ $$aFZJ-2025-01549
001038566 088__ $$2arXiv$$aarXiv:2403.07462
001038566 1001_ $$0P:(DE-HGF)0$$aDobrynin, Dmitrii$$b0
001038566 245__ $$aCompressed-sensing Lindbladian quantum tomography with trapped ions
001038566 260__ $$c2025
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001038566 3367_ $$2BibTeX$$aARTICLE
001038566 3367_ $$2DataCite$$aOutput Types/Working Paper
001038566 520__ $$aCharacterizing the dynamics of quantum systems is a central task for the development of quantum information processors (QIPs). It serves to benchmark different devices, learn about their specific noise, and plan the next hardware upgrades. However, this task is also very challenging, for it requires a large number of measurements and time-consuming classical processing. Moreover, when interested in the time dependence of the noise, there is an additional overhead since the characterization must be performed repeatedly within the time interval of interest. To overcome this limitation while, at the same time, ordering the learned sources of noise by their relevance, we focus on the inference of the dynamical generators of the noisy dynamics using Lindbladian quantum tomography (LQT). We propose two different improvements of LQT that alleviate previous shortcomings. In the weak-noise regime of current QIPs, we manage to linearize the maximum likelihood estimation of LQT, turning the constrained optimization into a convex problem to reduce the classical computation cost and to improve its robustness. Moreover, by introducing compressed sensing techniques, we reduce the number of required measurements without sacrificing accuracy. To illustrate these improvements, we apply our LQT tools to trapped-ion experiments of single- and two-qubit gates, advancing in this way the previous state of the art.
001038566 536__ $$0G:(DE-HGF)POF4-5221$$a5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522)$$cPOF4-522$$fPOF IV$$x0
001038566 588__ $$aDataset connected to arXivarXiv
001038566 7001_ $$0P:(DE-Juel1)184904$$aCardarelli, Lorenzo$$b1
001038566 7001_ $$0P:(DE-Juel1)179396$$aMüller, Markus$$b2$$eCorresponding author$$ufzj
001038566 7001_ $$0P:(DE-HGF)0$$aBermudez, Alejandro$$b3
001038566 909CO $$ooai:juser.fz-juelich.de:1038566$$pVDB
001038566 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179396$$aForschungszentrum Jülich$$b2$$kFZJ
001038566 9131_ $$0G:(DE-HGF)POF4-522$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5221$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Computing$$x0
001038566 9141_ $$y2025
001038566 920__ $$lyes
001038566 9201_ $$0I:(DE-Juel1)PGI-2-20110106$$kPGI-2$$lTheoretische Nanoelektronik$$x0
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