Diluted maximum-likelihood algorithm for quantum tomography

Abstract

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.

v2: Convergence proof added

References in corpus (3)

• Generation of a superposition of odd photon number states for quantum information networks
• Quantum homodyne tomography of a two-photon Fock state
• Tomographic test of Bell's inequality for a time-delocalized single photon

Cited by in corpus (8)

• Quantum memory for squeezed light
• Complete Characterization of Quantum-Optical Processes
• Knowledge and ignorance in incomplete quantum state tomography
• An effective method to estimate multidimensional Gaussian states
• Efficient decoherence-free entanglement distribution over lossy quantum channels
• Propagation of Squeezed Vacuum under Electromagnetically Induced Transparency
• Connections of geometric measure of entanglement of pure symmetric states to quantum state estimation
• Markov Chain Monte Carlo Estimation of Quantum States