Improved Tomographic Estimates by Specialized Neural Networks

Abstract: Characterization of quantum objects, being states, processes, or measurements, complemented by previous knowledge about them is a valuable approach, especially as it leads to routine procedures for real‐life components. To this end, machine learning algorithms have demonstrated to successfully operate in presence of noise, especially for estimating specific physical parameters. Here, it is shown that a neural network (NN) can improve the tomographic estimate of parameters by including a convolutional stage. This technique is applied to quantum process tomography for the characterization of several quantum channels. A stable and reliable operation is demonstrated that is achievable by training the network only with simulated data. The obtained results show the viability of this approach as an effective tool based on a completely new paradigm for the employment of NNs operating on classical data produced by quantum systems.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Improved Tomographic Estimates by Specialized Neural Networks ; day:27 ; month:06 ; year:2023 ; extent:11 ; Advanced quantum technologies ; (27.06.2023) (gesamt 11)

Contributors, Places and Time

• Creator: Guarneri, Massimiliano ; Gianani, Ilaria ; Barbieri, Marco ; Chiuri, Andrea

Further information

• DOI: 10.1002/qute.202300027
• URN: urn:nbn:de:101:1-2023062715143623322485
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:51 AM CEST