ref [2] · 2608.22379 · notice #10744 · dispute
Raw extraction · citation context
indirectly through the maximum likelihood estimate (MLE) of the underlying quantum state. Iterative maximum likelihood (MaxLik) estimates a physical density matrix from homodyne data, while the diluted MLE scheme improves the numerical convergence of the likelihood optimization [23,30,31]. Bayesian tomography infers a posterior distribution over density matrices, so the resulting estimate depends on the selected prior [2]. Convex optimization similarly reconstructs a positive, unit trace density matrix in a truncated Fock basis [5]. These methods provide numerical or posterior optimiza- tion procedures, but the cited works do not establish explicit sample dependent convergence rates for the reconstructed density matrix or Wigner function in a specified norm. Machine learning approaches,
Parser render (TeX stripped for reading; raw above is the evidence)
indirectly through the maximum likelihood estimate (MLE) of the underlying quantum state. Iterative maximum likelihood (MaxLik) estimates a physical density matrix from homodyne data, while the diluted MLE scheme improves the numerical convergence of the likelihood optimization [23,30,31]. Bayesian tomography infers a posterior distribution over density matrices, so the resulting estimate depends on the selected prior [2]. Convex optimization similarly reconstructs a positive, unit trace density matrix in a truncated Fock basis [5]. These methods provide numerical or posterior optimiza- tion procedures, but the cited works do not establish explicit sample dependent convergence rates for the reconstructed density matrix or Wigner function in a specified norm. Machine learning approaches