Microscopic parametrizations for gate set tomography under coloured noise
Gate set tomography (GST) allows for a self-consistent characterization of noisy quantum information processors (QIPs). The standard approach treats QIPs as black boxes only constrained by the laws of physics, attaining full generality at a considerable resource cost: numerous circuits must be run i...
| Authors: | , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repository: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::5c84e3dd84dca621cc9895377cd2d6f3 |
| Online Access: | http://hdl.handle.net/10261/429010 https://www.scopus.com/pages/publications/85218202435?origin=resultslist |
| Access Level: | Open access |
| Keyword: | Image segmentation Logic gates Markov processes Phase noise Photons Quantum computers Quantum noise Quantum optics White noise Black boxes Colored noise Considerable resources Gate sets Information processor Laws of physics Parametrizations Quantum gates Quantum Information Resource costs Trapped ions |
| Summary: | Gate set tomography (GST) allows for a self-consistent characterization of noisy quantum information processors (QIPs). The standard approach treats QIPs as black boxes only constrained by the laws of physics, attaining full generality at a considerable resource cost: numerous circuits must be run in order to amplify each of the gate set parameters. In this work, we show that a microscopic parametrization of quantum gates under time-correlated noise on the driving phase, motivated by recent experiments with trapped-ion gates, enables a more efficient version of GST. Adopting the formalism of filter functions over the noise spectral densities, we discuss the minimal parametrizations of the gate set that include the effect of non-Markovian quantum evolutions during the individual gates. We compare the estimated gate sets obtained by our method and the standard long-sequence GST, discussing their accuracies and showcasing the advantages of the parametrized approach in terms of the sampling complexity. © The Author(s) 2025. |
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