The content discusses the problem of quantifying the optimal performance of quantum entanglement distillation, a key task in quantum information theory. The main challenge lies in the asymptotic nature of the problem, where the performance of the protocol can be improved by using more copies of the given quantum state. This leads to expressions involving regularized formulas, which are extremely difficult to evaluate.
To overcome this issue, the authors propose a different approach by focusing on the error exponent of the distillation protocol, rather than the optimal yield. They establish a connection between entanglement distillation and a state discrimination task known as entanglement testing, which allows them to leverage information-theoretic techniques.
As the main contribution, the authors solve the generalized quantum Sanov's theorem, a problem that was previously unsolved even in classical information theory. They show that the optimal asymptotic error exponent of entanglement distillation is given by the reverse relative entropy of entanglement, a single-letter quantity that can be computed using only a single copy of the quantum state. This is a remarkable result, as it provides a computable formula for an operational measure of entanglement, circumventing the difficulties associated with regularized expressions.
The authors emphasize that this new approach to benchmarking entanglement distillation, focusing on the error exponent rather than the optimal yield, can help overcome major bottlenecks in the quantification of the performance of asymptotic entanglement manipulation protocols.
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arxiv.org
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