Generalized Reimpell-Werner Iteration
Abstract
Quantum measurements and channels determine how information is extracted, encoded, and transmitted in quantum protocols. Optimizing their performance often requires numerical methods that remain practical as Hilbert space dimensions increase. The Reimpell-Werner iteration offers a practical approach to these tasks through repeated matrix updates that respect the constraints. Here, we generalize this iteration to linear objectives with arbitrary Hermitian cost matrices. We prove that the iterates converge to a global optimum whenever the initialization satisfies suitable support overlap conditions. For each fixed problem, choice of iteration parameters, and admissible initialization, 𝒪(1/ε) iterations suffice asymptotically to bring the objective value within ε of the optimum. These results provide a rigorous foundation for the iteration and broaden the class of optimization problems to which its convergence guarantees apply.
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BibTeX
@misc{ru2026generalized,
title = {{Generalized Reimpell-Werner Iteration}},
author = {Shihao Ru and Bikun Li and Weibo Gao and Liang Jiang},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.40203},
eprint = {2609.40203},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.40203},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Generalized Reimpell-Werner Iteration
AU - Shihao Ru
AU - Bikun Li
AU - Weibo Gao
AU - Liang Jiang
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.40203
UR - https://arxiv.org/abs/2609.40203
AB - Quantum measurements and channels determine how information is extracted, encoded, and transmitted in quantum protocols. Optimizing their performance often requires numerical methods that remain practical as Hilbert space dimensions increase. The Reimpell-Werner iteration offers a practical approach to these tasks through repeated matrix updates that respect the constraints. Here, we generalize this iteration to linear objectives with arbitrary Hermitian cost matrices. We prove that the iterates converge to a global optimum whenever the initialization satisfies suitable support overlap conditions. For each fixed problem, choice of iteration parameters, and admissible initialization, 𝒪(1/ε) iterations suffice asymptotically to bring the objective value within ε of the optimum. These results provide a rigorous foundation for the iteration and broaden the class of optimization problems to which its convergence guarantees apply.
ER -