با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
محاسبات کوانتومی - Quantum-Computing
عنوان انگلیسی مقاله:
Algebraic Attacks on Block Ciphers Using Quantum Annealing
ترجمه فارسی عنوان مقاله:
حملات جبری به رمزهای بلوکی با استفاده از آنیل کوانتومی
ieee - ieee Transactions on Emerging Topics in Computing;2022;10;2;10:1109/TETC:2022:3143152
Elzbieta Burek; Michal Wronski; Krzysztof Mank; Michal Misztal
Drug combination is very common in the course of disease treatment. However, it inevitably
increases the overall risk of adverse drug reactions (ADRs). It is very important to early and accurately
detect and identify the potential ADRs for combined medication safety and public health. Social media is an
important pharmacovigilance data source for ADR detection. But the data are complex, mass, clutter,
highly sparse, so it is difficult to detect the ADR information from these data. Deep learning stands out in
terms of increased accuracy. However, it takes a lot of training time and requires a lot of computing power.
Quantum computing has strong parallel computing capability, and requires less computing power. By
introducing attention mechanism and quantum computing into Bi-directional Long Short-Term Memory
(Bi-LSTM), a quantum Bi-LSTM with attention (QBi-LSTMA) model is constructed for ADR detection
from social media big data. QBi-LSTMA is composed of 6 variable component subcircuits (VQC) stacked.
Under the condition that the main topology of Bi-LSTM remains unchanged, the biases of QBi-LSTMA in
input gate, forgetting gate, candidate memory unit and output gate are removed to simplify the network
structure, and the weight and active value qubits of the model are used to update the network weight. The
performance of the proposed method is evaluated on the SMM4H dataset, comparing with one traditional
ADR detection method and three deep learning based ADR detection approaches. The experiment results
show that the proposed method has great potential in ADRs detection. To the best of our knowledge, this is
the first time to investigate quantum computing to detect ADRs from social media big data.
INDEX TERMS: Social media big data | Adverse drug reactions (ADRs) | Bi-directional Long Short-Term Memory (Bi-LSTM) | Quantum Bi-LSTM with attention (QBi-LSTMA).