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https://doi.org/10.1109/TIT.2020.3021289
Title: | Noisy Quantum State Redistribution With Promise and the Alpha-Bit | Authors: | Anshu, Anurag Hsieh, Min-Hsiu Jain, Rahul |
Keywords: | Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Computer Science Engineering Alpha-bit state redistribution channel simulation communication with promise COMMUNICATION CAPACITY INFORMATION BOUNDS |
Issue Date: | 1-Dec-2020 | Publisher: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | Citation: | Anshu, Anurag, Hsieh, Min-Hsiu, Jain, Rahul (2020-12-01). Noisy Quantum State Redistribution With Promise and the Alpha-Bit. IEEE TRANSACTIONS ON INFORMATION THEORY 66 (12) : 7772-7786. ScholarBank@NUS Repository. https://doi.org/10.1109/TIT.2020.3021289 | Abstract: | We consider a variation of the well-studied quantum state redistribution task, in which the starting state is known only to the receiver Bob and not to the sender Alice. We refer to this as quantum state redistribution with a one-sided promise. In addition, we consider communication from Alice to Bob over a noisy channel N, instead of the noiseless channel, as is usually considered in state redistribution. We take a natural approach towards the solution of this problem where we 'embed' the promise as part of the state and then invoke known protocols for quantum state redistribution composed with known protocols for transfer of quantum information over noisy channels. Using our approach, we are able to reproduce the Alpha-bit capacities with or without entanglement assistance in Hayden and Penington, using known protocols for quantum state redistribution and quantum communication over noisy channels. Furthermore, we generalize the entanglement assisted classical Alpha-bit capacity, showing that any quantum state redistribution protocol can be used as a black box to simulate classical communication. | Source Title: | IEEE TRANSACTIONS ON INFORMATION THEORY | URI: | https://scholarbank.nus.edu.sg/handle/10635/192440 | ISSN: | 00189448 15579654 |
DOI: | 10.1109/TIT.2020.3021289 |
Appears in Collections: | Staff Publications Elements |
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