Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4661
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dc.contributor.authorGaikwad, Akshay-
dc.contributor.authorArvind-
dc.contributor.authorDorai, Kavita-
dc.date.accessioned2023-08-12T19:11:50Z-
dc.date.available2023-08-12T19:11:50Z-
dc.date.issued2021-
dc.identifier.citationQuantum Information Processing, 20(1).en_US
dc.identifier.urihttps://doi.org/10.1007/s11128-020-02930-z-
dc.identifier.urihttp://hdl.handle.net/123456789/4661-
dc.descriptionOnly IISERM authors are available in the record.en_US
dc.description.abstractWe use a constrained convex optimization (CCO) method to experimentally characterize arbitrary quantum states and unknown quantum processes on a two-qubit NMR quantum information processor. Standard protocols for quantum state and quantum process tomography are based on linear inversion, which often result in an unphysical density matrix and hence an invalid process matrix. The CCO method, on the other hand, produces physically valid density matrices and process matrices, with significantly improved fidelity as compared to the standard methods. We use the CCO method to estimate the Kraus operators and characterize gates in the presence of errors due to decoherence. We then assume Markovian system dynamics and use a Lindblad master equation in conjunction with the CCO method, to completely characterize the noise processes present in the NMR system.en_US
dc.language.isoen_USen_US
dc.publisherspringer linken_US
dc.subjectTrue experimentalen_US
dc.subjectreconstructionen_US
dc.subjectquantumen_US
dc.subjectstatesen_US
dc.titleTrue experimental reconstruction of quantum states and processes via convex optimizationen_US
dc.typeArticleen_US
Appears in Collections:Research Articles

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