Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 304

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Iterative maximum-likelihood reconstruction in quantum homodyne tomography

Open access
Lvovsky, A I·2004·Journal of Optics B: Quantum and Semiclassical Optics

Abstract

I propose an iterative expectation maximization algorithm for reconstructing the density matrix of an optical ensemble from a set of balanced homodyne measurements. The algorithm applies directly to the acquired data, bypassing the intermediate step of calculating marginal distributions. The advantages of the new method are made manifest by comparing it with the traditional inverse Radon transformation technique.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1088/1464-4266/6/6/014

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 23 references

Cited by

See all 304

Metadata

Authors
Lvovsky, A. I.
Venue
Journal of Optics B: Quantum and Semiclassical Optics
Published
2004-05-29
Volume
6
Issue
6
Pages
S556-S559
ISSN
  • 1464-4266 (ISSN-L)
  • 1741-3575
OpenAlex
W2167596506
Cited by
304
References
23
FWCI
7.79
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Iterative maximum-likelihood reconstruction in quantum homodyne tomography. https://app.cochat.ai/papers/10.1088/1464-4266/6/6/014
The paper
Lvovsky, A. I. (2004). Iterative maximum-likelihood reconstruction in quantum homodyne tomography. Journal of Optics B: Quantum and Semiclassical Optics. https://doi.org/10.1088/1464-4266/6/6/014

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.