Adverse Drug Reaction Detection From Social Media Based on Quantum Bi-LSTM With Attention

Wang, Xuqi; Wang, Xianfeng; Zhang, Shanwen

doi:10.1109/access.2022.3151900

Cited by 8 publications

(1 citation statement)

References 32 publications

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“…Through numerical simulations, experiments indicated that QLSTM learns faster and requires fewer epochs compared to classical LSTM with the same number of network parameters. Building upon Chen et al's research, researchers have made various improvements to QLSTM and applied it extensively in different domains such as speech recognition [11], ocean trajectory prediction [12], sentiment analysis [13], and more. To enhance prediction accuracy, Song et al [14] proposed a mogrifier-quantum weighted memory-enhanced Long Short-Term Memory neural network prediction model.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Quantum Long Short-Term Memory By Using Bidirectional Ring Variational Quantum Circuit

Qi,

Lv,

Gong

et al. 2024

Preprint

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With the rapid development of quantum machine learning, Quantum Long Short-Term Memory (QLSTM) have demonstrated quantum advantages in time series forecasting problems. Currently, it has found applications in fields such as financial market analysis, natural language processing, and weather forecasting, but its accuracy in regression problem prediction still shows deficiencies. To improve the prediction accuracy of the model, enhanced quantum long short-term memory by using bidirectional ring variational quantum circuit (EQLSTM) is proposed. We reconsidered and optimized the types, quantities, and arrangements of quantum gates, designing a bidirectional ring variational quantum circuit composed of CRX gates (Bi-VQC) to enhance its expressibility. Bi-VQC can more accurately describe and encode complex quantum state features, enabling the EQLSTM to better learn and represent the characteristics of the input data. In addition, Bi-VQC reduces the number of optimizable parameters and lowers the circuit load of the EQLSTM model. To validate the effectiveness of the proposed model, experiments are conducted to test and evaluate the EQLSTM using the Aggregated traffic in the United Kingdom academic network backbone. The experimental results confirm that the proposed EQLSTM model improves from 90.56\% to 97.57\% compared to the QLSTM model.

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Section: Introductionmentioning

confidence: 99%

Enhanced Quantum Long Short-Term Memory By Using Bidirectional Ring Variational Quantum Circuit

Qi,

Lv,

Gong

et al. 2024

Preprint

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A brief review on quantum computing based drug design

Das,

Ray,

Bhattacharyya

et al. 2024

WIREs Data Min & Knowl

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Design and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre‐existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug molecule to its commercialization in the market. One of the most critical steps in drug design is to find the molecular interactions between the target (infected) molecule and the drug molecule. Several complex chemical equations need to be solved to determine the molecular interactions. In the late 20th Century, the advancement of computational technologies has made the solution of chemical equations relatively easier and faster. Moreover, the design of drug molecules involves multi‐criteria optimization. Classical computational methodologies have been used for drug design since the end of the 20th Century. However, nowadays, more advanced computational methodologies are inevitable in designing drugs for new diseases and drugs with fewer side effects. In this context, the quantum computing paradigm has proved beneficial in drug design due to its advanced computational capabilities. This paper presents a state‐of‐the‐art comprehensive review of the quantum computing‐based methodologies involved in drug design. A comparative study is made about the different quantum‐aided drug design methods, stating each methodology's merits and demerits. The review work presented in this manuscript will help new researchers assess the present state‐of‐the‐art concept of quantum‐based drug design.This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Computational Intelligence Application Areas > Health Care

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Quantum deep neural networks for time series analysis

Padha,

Sahoo

2024

Quantum Inf Process

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Adverse Drug Reaction Detection From Social Media Based on Quantum Bi-LSTM With Attention

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 8 publications

References 32 publications

Enhanced Quantum Long Short-Term Memory By Using Bidirectional Ring Variational Quantum Circuit

Enhanced Quantum Long Short-Term Memory By Using Bidirectional Ring Variational Quantum Circuit

A brief review on quantum computing based drug design

Quantum deep neural networks for time series analysis

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