Lindblad-like quantum tomography for non-Markovian quantum dynamical maps
We introduce Lindblad-like quantum tomography (LℓQT) as a quantum characterization technique of time-correlated noise in quantum information processors. This approach enables the estimation of time-local master equations, including their possible negative decay rates, by maximizing a likelihood func...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::ea64c92da7121079405edbd0c7ca76e6 |
| Acceso en línea: | http://hdl.handle.net/10261/428929 https://www.scopus.com/pages/publications/105007460219?origin=resultslist |
| Access Level: | acceso abierto |
| Palabra clave: | Markov processes Quantum channel Quantum entanglement Quantum optics Characterization techniques Correlated noise Dephasing Dynamical maps Likelihood functions Lindblad Non-Markovian Quantum Information Quantum tomography Time-correlated Qubits |
| Sumario: | We introduce Lindblad-like quantum tomography (LℓQT) as a quantum characterization technique of time-correlated noise in quantum information processors. This approach enables the estimation of time-local master equations, including their possible negative decay rates, by maximizing a likelihood function subject to dynamical constraints. We discuss LℓQT for the dephasing dynamics of single qubits in detail, which allows for a neat understanding of the importance of including multiple snapshots of the quantum evolution in the likelihood function, and how these need to be distributed in time depending on the noise characteristics. By a detailed comparative study employing both frequentist and Bayesian approaches, we assess the accuracy and precision of LℓQT of a dephasing quantum dynamical map that goes beyond the Lindblad limit, focusing on two different microscopic noise models that can be realised in either trapped-ion or superconducting-circuit architectures. We explore the optimization of the distribution of measurement times to minimize the estimation errors, assessing the superiority of each learning scheme conditioned on the degree of non-Markovinity of the noise, and setting the stage for future experimental designs of non-Markovian quantum tomography. © The Author(s) 2025. |
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