Sep 21, 2023 · In this work, we consider the tasks of reconstructing and classifying quantum states corrupted by the action of an unknown noisy channel using classical ...
Jul 2, 2024 · In this work, we consider the tasks of reconstructing and classifying quantum states corrupted by the action of an unknown noisy channel using classical feed- ...
In this work, we leverage machine learning techniques based on feed-forward neural networks to deal with the task of recovering noise-free quantum states when ...
In this approach, the effect of the noise on observables of interest can be mitigated using multiple measurements without additional hardware overhead.
In this work, we consider the tasks of reconstructing and classifying quantum states corrupted by the action of an unknown noisy channel using classical feed- ...
This repository contains the code implementation of the paper titled "Quantum State Reconstruction in a Noisy Environment via Deep Learning" ...
Sep 27, 2021 · We apply deep-neural-network-based techniques to quantum state classification and reconstruction. Our methods demonstrate high classification accuracies and ...
Co-authors ; Quantum state reconstruction in a noisy environment via deep learning. AR Morgillo, S Mangini, M Piastra, C Macchiavello. Quantum Machine ...
Aug 30, 2024 · Speaker: Brian Kirby (United States Army Research Laboratory) Date: August 29, 2024 10th International Conference on Quantum Information and ...
Missing: noisy environment
Nov 16, 2022 · Our approach, matrix-exponentiated gradient (MEG) tomography, is an online tomography method that allows for state tracking, updates the estimated density ...
Missing: deep | Show results with:deep