In this paper, we develop compositional vector-based semantics of positive transitive sentences using quantum natural language processing (Q-NLP) to compare the parametrized quantum circuits of two synonymous simple sentences in English and Persian. We propose a protocol based on quantum long short-term memory (Q-LSTM) for Q-NLP to perform various tasks in general but specifically for translating a sentence from English to Persian. Then, we generalize our method to use quantum circuits of sentences as an input for the Q-LSTM cell. This enables us to translate sentences in different languages.Our work paves the way toward representing quantum neural machine translation, which may demonstrate quadratic speedup and converge faster or reaches a better accuracy over classical methods.
Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computing can be used to implement many existing problems in different fields incredibly effectively. One important field that quantum computing has shown great results in machine learning. Until now, many different quantum algorithms have been presented to perform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will be reduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computation time, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since these algorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms of quantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimize quantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach including different optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms for big data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original ones and by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% in different circuits respectively. This is the amount of reduction for one iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the main circuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost and performance are improved.
Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. Itmakes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computingcan be used to implement many existing problems in different fields incredibly effectively. One important field that quantumcomputing has shown great results in machine learning. Until now, many different quantum algorithms have been presented toperform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will bereduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computationtime, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since thesealgorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms ofquantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimizequantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach includingdifferent optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms forbig data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original onesand by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% indifferent circuits and the number of time steps is reduced by three and 15 units, respectively. This is the amount of reduction forone iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the maincircuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost andperformance are improved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.