In the Quantum Internet, multipartite entanglement enables a rich and dynamic overlay topology, referred to as artificial topology, upon the physical one, that can be exploited for communication purposes. In fact, the ability to extractn-qubits GHZ states and EPR pairs from the original multipartite entangled state constitutes the resource primitives for end-to-end and on-demand quantum communications. Thus, in this paper, we theoretically determine upper and lower bounds for the number of extractablen-qubits GHZ states and EPR pairs involving nodes remote in the artificial topology, as well as the achievable sizenof remote GHZ states. The theoretical analysis is then complemented by the proposal of a novel algorithm, which provides in polynomial-time a heuristic solution to the above problem. This is remarkable, since the theoretical problem is NP-complete. The performance analysis demonstrates the proposed algorithm is able to effectively manipulate the original and arbitrary graph state for extracting entanglement resources across remote nodes.

Multipartite entanglement plays a crucial role in the Quantum Internet design, due to its potentiality of significantly increasing the network performance. In this paper, we identify the four key network functionalities for managing multipartite entanglement among remote nodes. And we discuss each functionality by considering - as case study - a specific multipartite state, which exhibits an attractive computing feature. Specifically, the designed state allows an arbitrary entangled node to calculate - in a distributed way - the sum of a set of values arbitrarily selected by the remaining entangled nodes.

MOTIVATION: T-cell receptors (TCRs) function to recognize antigens and play vital roles in T-cell immunology. Surveying TCR repertoires by characterizing complementarity-determining region 3 (CDR3) is a key issue. Due to the high diversity of CDR3 and technological limitation, accurate characterization of CDR3 repertoires remains a great challenge. RESULTS: We propose a computational method named CATT for ultra-sensitive and precise TCR CDR3 sequences detection. CATT can be applied on TCR sequencing, RNA-Seq and single-cell TCR(RNA)-Seq data to characterize CDR3 repertoires. CATT integrated de Bruijn graph-based micro-assembly algorithm, data-driven error correction model and Bayesian inference algorithm, to self-adaptively and ultra-sensitively characterize CDR3 repertoires with high performance. Benchmark results of datasets from in silico and experimental data demonstrated that CATT showed superior recall and precision compared with existing tools, especially for data with short read length and small size and single-cell sequencing data. Thus, CATT will be a useful tool for TCR analysis in researches of cancer and immunology. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.life.hust.edu.cn/CATT or https://github.com/GuoBioinfoLab/CATT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.