On converse bounds for classical communication over quantum channels

We explore several new converse bounds for classical communication over quantum channels in both the one-shot and asymptotic regimes. First, we show that the Matthews-Wehner meta-converse bound for entanglement-assisted classical communication can be achieved by activated, no-signalling assisted codes, suitably generalizing a result for classical channels. Second, we derive a new efficiently computable meta-converse on the amount of classical information unassisted codes can transmit over a single use of a quantum channel. As applications, we provide a finite resource analysis of classical communication over quantum erasure channels, including the second-order and moderate deviation asymptotics. Third, we explore the asymptotic analogue of our new meta-converse, the $\Upsilon$-information of the channel. We show that its regularization is an upper bound on the classical capacity, which is generally tighter than the entanglement-assisted capacity and other known efficiently computable strong converse bounds. For covariant channels we show that the $\Upsilon$-information is a strong converse bound.Comment: v3: published version; v2: 18 pages, presentation and results improve

Additional file 4: Table S3. of Evolution, gene expression profiling and 3D modeling of CSLD proteins in cotton

Comparison of ML and Bayesian trees based on three alignments (Kalign, Mafft and Muscle) using Ktreedist. (DOCX 33 kb

Additional file 10: Table S15. of Evolution, gene expression profiling and 3D modeling of CSLD proteins in cotton

Model validation scores of the full-length GrCSLD1 protein. (DOCX 22 kb

Additional file 5: Figure S1. of Evolution, gene expression profiling and 3D modeling of CSLD proteins in cotton

The different topologies of cotton CSLD trees reconstructed from ML and Bayesian based on three alignments and the elision strategy. Support values are shown for A. thaliana-cotton and cotton CSLD nodes using different color circles as bootstrap proportions/SH-like aLRT scores/Bayesian posterior probabilities. The cotton CSLD protein clades are indicated by different colors. “Other CSLD” indicates the CSLD proteins from other plant species. (TIFF 2007 kb

Additional file 11: Table S4, 5 and 6. of Evolution, gene expression profiling and 3D modeling of CSLD proteins in cotton

The source of transcriptome data from G. hirsutum, G. arboreum and G. raimondii. (XLSX 12 kb

Additional file 9: Figure S4. of Evolution, gene expression profiling and 3D modeling of CSLD proteins in cotton

Multiple sequence alignments of GrCSLD1, GhCESA1, BcsA and ATCSLD1. The secondary structure of GrCSLD1 was calculated using the DSS algorithm of PyMOL. The violet cylinders, yellow arrows, and black lines indicate the α-helices, β-strand and coil of GrCSLD1; the red rectangles and yellow rectangles indicate the α-helices and β-strand of GhCESA1, and the red lines and yellow lines indicate the α-helices and β-strand of BcsA. The plant-conserved region (P-CR) and class-specific region (CSR) are highlighted with blue and green lines. Large red letters indicate sites of episodic positive selection in GrCSLD1. (TIFF 4834 kb

Additional file 14: Table S14. of Evolution, gene expression profiling and 3D modeling of CSLD proteins in cotton

The relative expression level of CSLD genes of G. hirsutum by comparative 2-ΔΔCT method using qRT-PCR. (XLSX 10 kb