Quantum error correction codes (QECCs) play a central role both in quantum communications and in quantum computation, given how error-prone quantum technologies are. Practical quantum error correction codes, such as stabilizer codes, are generally structured to suit a specific use, and present rigid code lengths and code rates, limiting their adaptability to changing requirements. This paper shows that it is possible to both construct and decode QECCs that can attain the maximum performance of the finite blocklength regime, for any chosen code length and when the code rate is sufficiently high.A recently proposed strategy for decoding classical codes called GRAND (guessing random additive noise decoding) opened doors to decoding classical random linear codes (RLCs) that perform near the capacity of the finite blocklength regime. By making use of the noise statistics, GRAND is a noise-centric efficient universal decoder for classical codes, providing there is a simple code membership test. These conditions are particularly suitable for quantum systems and therefore the paper extends these concepts to quantum random linear codes (QRLCs), which were known to be possible to construct but whose decoding was not yet feasible. By combining QRLCs and a newly proposed quantum GRAND, this paper shows that decoding versatile quantum error correction is possible, allowing for QECCs that are simple to adapt on the fly to changing conditions. The paper starts by assessing the minimum number of gates in the coding circuit needed to reach the QRLCs' asymptotic performance, and subsequently proposes a quantum GRAND algorithm that makes use of quantum noise statistics, not only to build an adaptive code membership test, but also to efficiently implement syndrome decoding.
Abstract-With the increase in demand to Backbone networks, one became fundamental the application of new telecommunication technologies for efficient use of devices and optical links, such as xPSK modulations with dual polarization, dispersion compensating fibers, coherent detection and digital processing signals. Thus, network planning using analytical models have been proposed in the last years for this purpose. In this paper we propose the use of numerical simulations in wavelength-division multiplexing networks planning via a novel iterative method with highperformance processing, which does an analysis of the quality of transmission in transparent networks.
The quantum internet promises to extend entanglement correlations from nearby neighbors to any two nodes in a network. How to efficiently distribute entanglement over large-scale networks is still an open problem that greatly depends on the technology considered. In this work, we consider quantum networks composed of photonic channels characterized by a trade-off between the entanglement generation rate and fidelity. For such networks we look at the two following problems: the one of finding the best path to connect any two given nodes in the network bipartite entanglement routing, and the problem of finding the best starting node in order to connect three nodes in the network multipartite entanglement routing. We consider two entanglement distribution models: one where entangled qubit are distributed one at a time, and a flow model where a large number of entangled qubits are distributed simultaneously. We propose the use of continuous fidelity curves (i.e., entanglement generation fidelity vs rate) as the main routing metric. Combined with multi-objective path-finding algorithms, the fidelity curves describing each link allow finding a set of paths that maximize both the end-to-end fidelity and the entanglement generation rate. For the models and networks considered, we prove that the algorithm always converges to the optimal solution, and we show through simulation that its execution time grows polynomial with the number of nodes in the network. Our implementation grows with the number of nodes with a power between 1 and 1.4 depending on the network. This work paves the way for the development of path-finding algorithms for networks with complex entanglement distribution protocols, in particular for other protocols that exhibit a trade-off between generation fidelity and rate, such as repeater-and-purify protocols.
Business Process Modeling is an activity increasingly adopted by organizations seeking to improve their operational performance. A common practice for this activity is to performe interviews and workshops by involving modeling professionals (modelers) and domain experts. The produced models, in this case, are built based on the interpretation of the views gathered from domain experts. Considering this context, this paper discusses an approach for automated interactions between a computational agent and domain experts with the aim of determining process' control flow constraints, preventing potential inconsistencies and identifying modeling alternatives. The interaction generation mechanism, which is based on the building of the precess's discernment structure, provides to the computational agent the complete view of the modeling solution space. As a proof of concept, a prototype to support interactions was developed. The performed tests and experiments demonstrate that interactions produce valid and consistent models and allow the analysis of modeling alternatives. Resumo: A Modelagem de Processos de Negócio é uma atividade cada vez mais adotada por organizações que buscam melhorar seu desempenho operacional. Uma prática comum para esta atividade consiste na identificação do comportamento dos processos e sua transcrição em modelos, ao longo de entrevistas e oficinas envolvendo profissionais de modelagem (modeladores) e especialistas de domínio. Os modelos produzidos, neste caso, são construídos por modeladores tendo como base sua interpretação das visões colhidas junto a especialistas de domínio. Considerando este contexto, este trabalho discute um modelo de interações automatizadas com especialistas de domínio para capturar restrições de controle do fluxo do processo. O modelo concebido para apoiar as interações baseou-se na construção de uma estrutura de discernimento, que fornece a visão de todo o Espaço de Solução para o processo. Como prova de conceito foi desenvolvido um protótipo para interações entre um agente computacional e um humano. Conforme verificado em testes e experimentos com o protótipo, as interações permitem a análise de alternativas de modelos, produzem modelos consistentes e avaliam todo o Espaço de Solução de modelagem do processo.
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