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- W2793017974 abstract "Natural Language Processing and Semantic Web include several NP complete/hard problems that are intractable for classical computing machines. Even though distributed computing has provided remarkable advances (more precisely in dealing with big data), non-decomposable NP problems are still intractable in many real-world applications. And, from quantum computing perspective, solving complex problems with universal quantum gates requires developing of quantum algorithms. Considering commercializing quantum annealing machines by D-Wave, achieving global optimum for discrete optimization problems has been realized. In this study, a novel approach has been introduced to convert symbolic AI problems into quadratic unconstrained binary optimization (QUBO) form. More narrowly, this method represents classification of text documents (fragments) as optimizing a QUBO function. After embedding the train corpus into a QUBO function, D-Wave quantum annealer is used to classify new observations with finding the minimum energy level of the system." @default.
- W2793017974 created "2018-03-29" @default.
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- W2793017974 date "2018-02-21" @default.
- W2793017974 modified "2023-09-26" @default.
- W2793017974 title "Quantum Artificial Intelligence for Natural Language Processing Applications" @default.
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- W2793017974 doi "https://doi.org/10.1145/3159450.3162338" @default.
- W2793017974 hasPublicationYear "2018" @default.
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