We propose an efficient quantum state tomography method inspired by compressed sensing and threshold quantum state tomography that can drastically reduce the number of measurement settings to reconstruct the density matrix of an N-qudit system. We validate our algorithm with simulations on IBMQ and demonstrate the efficient and accurate reconstruction of N≤7 qubit systems, reproducing GHZ, W, and random states with O(1), O(N2), and O(N) settings.
@article{arxiv.2502.10031,
title = {Enhanced Compressive Threshold Quantum State Tomography for Qudit Systems},
author = {Giovanni Garberoglio and Maurizio Dapor and Diego Maragnano and Marco Liscidini and Daniele Binosi},
journal= {arXiv preprint arXiv:2502.10031},
year = {2025}
}
Comments
13 pages, 6 figures. Python implementation available at https://github.com/gioGarbe/ECT-QST