Diluted maximum-likelihood algorithm for quantum tomography

By J. Řeháček, Z. Hradil, Emanuel Knill, Alexander I. Lvovsky. 2006

187 citations recorded by the source corpus.

We propose a refined iterative likelihood-maximization algorithm for reconstructing a quantum state from a set of tomographic measurements. The algorithm is characterized by a very high convergence rate and features a simple adaptive procedure that ensures likelihood increase in every iteration and convergence to the maximum-likelihood state. We apply the algorithm to homodyne tomography of optical states and quantum tomography of entangled spin states of trapped ions and investigate its convergence properties.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/4b1ccf09a08f79e9a93866033c4e29c1be2c9ac3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. E. Bimbard
2. Nitin Jain
3. A. MacRae
4. Alexander I. Lvovsky
5. Michael G. Raymer

In Atomic and Molecular Physics, and Optics

1. E. Bimbard
2. Nitin Jain
3. A. MacRae
4. Alexander I. Lvovsky
5. Michael G. Raymer

Citations in the corpus

• Quantum-optical state engineering up to the two-photon level (2009, methodology): “The phase-quadrature pairs from the acquisition were then fed to a maximum-likelihood reconstruction algorith”