Saved in:
Bibliographic Details
Main Authors: Grove, Logan W., Barge, Pratik J., Jacob, Kevin Valson
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2503.13698
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866909769746350080
author Grove, Logan W.
Barge, Pratik J.
Jacob, Kevin Valson
author_facet Grove, Logan W.
Barge, Pratik J.
Jacob, Kevin Valson
contents Characterizing quantum processes is indispensable for the implementation of any task in quantum information processing. In this paper, we develop an efficient method to fully characterize arbitrary Gaussian processes in continuous-variable quantum systems. This is done by directly obtaining all elements of the symplectic matrix that describes the process. Only Gaussian resources such as coherent probes and quadrature measurements are needed for this task. The method is efficient, involving only $O(N^2)$ steps to characterize an $N$-mode system. Further, the method is resilient to uniform loss. We simulate this procedure using the Python package Strawberry Fields. We observe that heterodyne measurements outperform homodyne measurements for reconstructing Gaussian processes.
format Preprint
id arxiv_https___arxiv_org_abs_2503_13698
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Characterizing Gaussian quantum processes with Gaussian resources
Grove, Logan W.
Barge, Pratik J.
Jacob, Kevin Valson
Quantum Physics
Characterizing quantum processes is indispensable for the implementation of any task in quantum information processing. In this paper, we develop an efficient method to fully characterize arbitrary Gaussian processes in continuous-variable quantum systems. This is done by directly obtaining all elements of the symplectic matrix that describes the process. Only Gaussian resources such as coherent probes and quadrature measurements are needed for this task. The method is efficient, involving only $O(N^2)$ steps to characterize an $N$-mode system. Further, the method is resilient to uniform loss. We simulate this procedure using the Python package Strawberry Fields. We observe that heterodyne measurements outperform homodyne measurements for reconstructing Gaussian processes.
title Characterizing Gaussian quantum processes with Gaussian resources
topic Quantum Physics
url https://arxiv.org/abs/2503.13698