Rapid solution of logical equivalence problems by quantum computation algorithm

Zidan, Mohammed; Hegazy, Salem F.; Abdel‐Aty, Mahmoud; Obayya, S. S. A.

doi:10.1016/j.asoc.2022.109844

Cited by 18 publications

(2 citation statements)

References 26 publications

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“…We use numerical techniques to incorporate real influenza infection data into the system (8) in order to accurately anticipate the spread of the disease. We use the method outlined in [18,49,50,51] to estimate the parameters and numerically solve the system. Table 1 contains the estimated and modified parameter values.…”

Section: Estimation Of Parameter Valuesmentioning

confidence: 99%

Untitled

2024

Appl. Math. Inf. Sci.

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This article offers a condensed analysis of a fractional SEIR model used to track and forecast influenza spread. In addition, the seasonal evolution of influenza epidemics is predicted using the ARIMA model. The Atangana-Baleanu-Caputo (ABC) fractional derivative operator, which offers a more precise explanation of influenza dynamics than conventional integer operators, is the subject of the study. The study shows that the fractional order model outperforms the ARIMA model and agrees with actual data. The results emphasize the significance of using fractional models to health risk management and creating numerical methods with accurate parameter values. Graphical illustrations of several fractional orders are included in the article. Graphical illustrations of various fractional order and fractal dimension levels are included in the article. These findings have significance for creating pandemic mathematical models that are more accurate and devising realistic influenza control methods.

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Section: Estimation Of Parameter Valuesmentioning

confidence: 99%

Untitled

2024

Appl. Math. Inf. Sci.

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“…The emergence of quantum-inspired techniques has opened up new avenues for addressing the limitations of CMLs 8 . These techniques, inspired by QC principles, leverage the inherent parallelism and quantum-inspired optimization algorithms to enhance predictive capabilities.…”

Section: Introductionmentioning

confidence: 99%

QAmplifyNet: pushing the boundaries of supply chain backorder prediction using interpretable hybrid quantum-classical neural network

Jahin,

Shovon,

Islam

et al. 2023

Sci Rep

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Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy. Our proposed model demonstrates remarkable accuracy in predicting backorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspired techniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark dataset establish QAmplifyNet’s superiority over eight classical models, three classically stacked quantum ensembles, five quantum neural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal for supply chain management. We evaluate seven preprocessing techniques, selecting the best one based on logistic regression’s performance on each preprocessed dataset. The model’s interpretability is enhanced using Explainable artificial intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet also achieved the highest F1-score of 94% for predicting “Not Backorder” and 75% for predicting “backorder,” outperforming all other models. It also exhibited the highest AUC-ROC score of 79.85%, further validating its superior predictive capabilities. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.

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