期刊
IEEE TRANSACTIONS ON INFORMATION THEORY
卷 67, 期 2, 页码 946-960出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIT.2020.3034471
关键词
Channel capacity; Noise measurement; Convergence; Optimization; Approximation algorithms; Algorithms; channel capacity; entropy; information theory; quantum mechanics
资金
- Swiss National Science Foundation [200020-165843]
- National Centre of Competence in Research Quantum Science and Technology (QSIT)
This work generalizes classical Blahut-Arimoto algorithms to the quantum setting, providing efficient iterative schemes to compute various quantum channel capacities, with rigorous bounds on estimation error using quantum entropy inequalities and demonstration of fast convergence in numerical experiments.
The capacity of noisy quantum channels characterizes the highest rate at which information can be reliably transmitted and it is therefore of practical as well as fundamental importance. Capacities of classical channels are computed using alternating optimization schemes, called Blahut-Arimoto algorithms. In this work, we generalize classical Blahut-Arimoto algorithms to the quantum setting. In particular, we give efficient iterative schemes to compute the capacity of channels with classical input and quantum output, the quantum capacity of less noisy channels, the thermodynamic capacity of quantum channels, as well as the entanglement-assisted capacity of quantum channels. We give rigorous a priori and a posteriori bounds on the estimation error by employing quantum entropy inequalities and demonstrate fast convergence of our algorithms in numerical experiments.
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