Compressed-sensing Lindbladian quantum tomography with trapped ions
Characterizing 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...
| 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_::99ae72101242ed8216112a522821e3e3 |
| Acceso en línea: | http://hdl.handle.net/10261/427312 https://www.scopus.com/inward/record.uri?eid=2-s2.0-105016865264&doi=10.1088%2F2058-9565%2Fae0363&partnerID=40&md5=27764f202e4f7e0eaf5474c7f67ddf60 |
| Access Level: | acceso abierto |
| Palabra clave: | compressed sensing techniques Lindbladian quantum tomography (LQT) maximum likelihood estimation noise benchmarking quantum information processors (QIPs) Benchmarking Compressed sensing Constrained optimization Noise generators Quantum noise Quantum optics Qubits Trapped ions Compressed sensing technique Compressed-Sensing Information processor Lindbladian quantum tomography Maximum-likelihood estimation Noise benchmarking Quantum Information Quantum information processor Quantum tomography Sensing techniques Maximum likelihood estimation |
| Sumario: | Characterizing 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. © 2025 The Author(s). Published by IOP Publishing Ltd. |
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