Quick Navigation

Topics

Trapped Ion Quantum Computing

Pairwise Liouvillian learning from randomized measurements: practical aspects and guidelines for operating the protocol in large-scale experiments

arXiv
Authors: William T. Lam, Manoj K. Joshi, Daniel Stilck França, Benoît Vermersch

Year

2026

Paper ID

67440

Status

Preprint

Abstract Read

~2 min

Abstract Words

77

Citations

N/A

Abstract

We review and numerically study a protocol for Liouvillian learning based on randomized Pauli states and measurements. In particular, in the two-body, long-range interactions, and single-body noise setting, we describe the complete workflow to obtain the coefficients of the Liouvillian in an efficient and pairwise manner, meaning that the required classical memory is independent of the system size. We also provide guidelines for choosing the parameters for data acquisition and postprocessing that minimize the total reconstruction error.

Why This Paper Matters

  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • We review and numerically study a protocol for Liouvillian learning based on randomized Pauli states and measurements.

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

Local Citation Graph (Related-Paper Links)

Current Paper #67440 #69599 Tensor network compression usin... #69595 Tantalum as a base material for... #69590 Quantum Simulation of Spin-Depe... #69589 An integrated ultrahigh vacuum ...

External citation index: OpenAlex citation signal

Community Reactions

Quick sentiment from readers on this paper.

Score: 0
Likes: 0 Dislikes: 0

Sign in to react to this paper.

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.