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- W4384135047 abstract "The natural language processing (NLP) demands high computing resourses to process language related problems. The evolution of quantum computing hardware and simulators create avenues to design and develop NLP methods to test on these platforms. This research uses quantum natural language processing (QNLP) to simulate the sentence classification and compares optimization techniques (Simultaneous perturbation stochastic approximation: SPSA and convergent optimization via most-promising-area stochastic search: COMPASS) for small dataset and simple sentence structures and the results are promising to show that with the COMPASS optimization technique the model performs better Table 1. When the ansatz depth is less and vice-versa. The research in quantum natural language processing is infancy, the simulation results are promising and the better results can be expected for large datasets with different sentence structures if real quantum computers are available in near future." @default.
- W4384135047 created "2023-07-14" @default.
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- W4384135047 date "2023-01-01" @default.
- W4384135047 modified "2023-09-27" @default.
- W4384135047 title "Sentence Classification Using Quantum Natural Language Processing and Comparison of Optimization Methods" @default.
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- W4384135047 doi "https://doi.org/10.1007/978-3-031-35644-5_7" @default.
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