Multicontrolled unitary gates have been a subject of interest in quantum computing since their conception and are widely used in quantum algorithms. The current state-of-the-art approach to implementing$n$-qubit multicontrolled gates with a single target without relying on auxiliary qubits or approximate results involves the use of a quadratic number of single-qubit and CNOT gates. However, linear solutions are possible for the case where the controlled gate is special unitary, SU(2). The decomposition of an$n$-qubit multicontrolled SU(2) gate requires a circuit with a number of CNOT gates proportional to$28n$. In this work, we present a new decomposition of$n$-qubit multicontrolled SU(2) gates that require a circuit with a number of CNOT gates proportional to$20n$and proportional to$16n$if the SU(2) gate has at least one real-valued diagonal. The proposed algorithms produce the most efficient known circuits and improve the existing algorithm by reducing the number of CNOT gates and the overall circuit depth. As an application, we show the use of this decomposition for sparse quantum state preparation. Our results are further validated by demonstrating a proof of principle on a quantum device accessed through quantum cloud services.

The unprecedented growth of textual content on the Web made essential the development of automatic or semi-automatic techniques to help people to find valuable information in such a huge heap of text data. Automatic text summarization is one of such techniques that is being pointed out as offering a viable solution in such a chaotic scenario. Extractive text summarization, in particular, selects a set of sentences from a text according to specific criteria. Strategies for extractive summarization can benefit from preprocessing techniques that emphasize the relevance or infor-mativeness of sentences with respect to the selection criteria. This paper tests such a hypothesis using sentence simplification methods. Four methods are used to simplify a corpus of news articles in English: a rule-based method, an optimization method, a supervised deep learning model and an unsupervised deep learning model. The simplified outputs are summarized using 14 sentence selection strategies. The combinations of simplification and summarization methods are compared with the baseline --- the summarized corpus without previous simplification --- with a quantitative analysis, which suggests sentence compression with restrictions and models learned from large parallel corpora tend to perform better and yield gains over summarization without prior simplification.