Gradient-based stopping rules for maximum-likelihood quantum-state tomography
Gradient-based stopping rules for maximum-likelihood quantum-state tomography
When performing maximum-likelihood quantum-state tomography, one must find the quantum state that maximizes the likelihood of the state given observed measurements on identically prepared systems. The optimization is usually performed with iterative algorithms. This paper provides a gradient-based upper bound on the ratio of the true maximum likelihood and the …