Improved Tomographic Estimates by Specialized Neural Networks
Year: 2023
Authors: Guarneri M., Gianani I., Barbieri M., Chiuri A.
Autors Affiliation: ENEA Ctr Ric Frascati, Via E Fermi 45, I-00044 Frascati, Italy; Univ Roma Tre, Dipartimento Sci, Via Vasca Navale 84, I-00146 Rome, Italy; Ist Nazl Ottica, CNR, Largo E Fermi 6, I-50125 Florence, Italy.
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.
Journal/Review: ADVANCED QUANTUM TECHNOLOGIES
Volume: 6 (8) Pages from: to:
More Information: The authors thank P. Mataloni for granting access to the data and C. Macchiavello for fruitful discussion. This work was supported by the European Commission (FET-OPEN-RIA STORMYTUNE, grant agreement no. 899587), and by the NATO SPS Project HADES – MYP~G5839.KeyWords: neural networks; process tomography; quantum channelsDOI: 10.1002/qute.202300027ImpactFactor: 4.400Citations: 1data from “WEB OF SCIENCE” (of Thomson Reuters) are update at: 2024-09-15References taken from IsiWeb of Knowledge: (subscribers only)
