Application of Quantum Natural Language Processing for Language Translation

Abbaszade, Mina; Salari, Vahid; Mousavi, S. Sh.; Zomorodi‐Moghadam, Mariam; Zhou, Xujuan

doi:10.1109/access.2021.3108768

Cited by 32 publications

(14 citation statements)

References 51 publications

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“…Furthermore, in natural language processing, there have been related works based on quantum neural networks 26 – 29 . However, this part of the research is based on lexical analysis, which generates quantum circuits for each sentence based on tensor networks.…”

Section: Resultsmentioning

confidence: 99%

A new method of software vulnerability detection based on a quantum neural network

Zhou¹,

Pang²,

Feng³

et al. 2022

Sci Rep

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In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%.

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Section: Resultsmentioning

confidence: 99%

A new method of software vulnerability detection based on a quantum neural network

Zhou¹,

Pang²,

Feng³

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In the field of quantum machine learning (QML), applications of VQCs to standard machine learning tasks have achieved various degrees of success. Prominent examples include function approximation [13,[43][44][45], classification [13,14,[46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], generative modeling [64][65][66][67][68], deep RL [29][30][31][32][33][69][70][71][72], sequence modeling [43,[73][74][75][76], speech recognition [77], metric and embedding learning [78,79], transfer learning [50,80] and federated learning [...…”

Section: Variational Quantum Circuitsmentioning

confidence: 99%

Variational quantum reinforcement learning via evolutionary optimization

Chen

Huang

Hsing

et al. 2022

Mach. Learn.: Sci. Technol.

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Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here we present two frameworks of deep quantum RL tasks using a gradient-free evolution optimization: First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using the amplitude encoding; Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL on the MiniGrid environment with 147-dimensional inputs. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

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“…There are other works by Zen et al 29 that have used transmission learning toward scalable quantum neural network states using transmission learning. A protocol was proposed in 47 for machine translation based on quantum long short term memory for translating a sentence from English to Persian. In another work, Mishra et al 30 used the design and operation of a classical neural network and they designed a quantum neural network capable of working on a 10 qubit system.…”

Section: Related Workmentioning

confidence: 99%

An optimizing method for performance and resource utilization in quantum machine learning circuits

Salehi

Zomorodi‐Moghadam

Pławiak

et al. 2022

Sci Rep

Self Cite

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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.

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Application of Quantum Natural Language Processing for Language Translation

Cited by 32 publications

References 51 publications

A new method of software vulnerability detection based on a quantum neural network

A new method of software vulnerability detection based on a quantum neural network

Variational quantum reinforcement learning via evolutionary optimization

An optimizing method for performance and resource utilization in quantum machine learning circuits

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