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<p>In the evolving landscape of data-driven business intelligence, identifying the critical factors influencing marketing and production decisions remains a complex challenge due to the dynamic nature of customer behavior, product bundling strategies, and campaign performance metrics. This study introduces a robust Hamiltonian dense quantum nested generative Lyrebird adversarial attention networks framework leveraging the Marketing-and-Product-Performance-Dataset, which comprises 10,000 records capturing multifaceted dimensions such as revenue generation, subscription tiers, flash sales, bundle dynamics, and post-refund satisfaction. To enhance the decision-making process, data pre-processing is performed using Cumulative Curve Fitting Approximation (CCFA), followed by feature extraction through Dual-Aggregation Transformer (DuAT) that learns causal relationships across features. The final decision-making phase employs the proposed Hamiltonian Dense Quantum Nested Generative Lyrebird Adversarial Attention Network (HamDQN-GL2AN), a novel hybrid model that integrates Hamiltonian Quantum Generative Adversarial Networks (HQuGANs) with Dense Nested Attention Networks (DNA-Net), where learning parameters are optimized using the Lyrebird Optimization Algorithm (LOA). This integrated architecture effectively captures dense quantum interactions and nested attention cues across the dataset, resulting in a highly accurate classification and recommendation system. The classification objective of the proposed framework is to categorize marketing and production performance into predefined decision classes, namely High Performance, Moderate Performance, and Low Performance, based on aggregated business indicators such as conversion rate, revenue generation, ROI, customer satisfaction, and sales effectiveness. The proposed system, therefore, functions as both a predictive classification and business recommendation framework for intelligent marketing and production optimization. The proposed model achieves 99.9% accuracy, demonstrating superior performance in predictive analytics. Advantages of this framework include significantly enhanced sensitivity to nonlinear feature dependencies and reduced decision ambiguity through quantum-aware adversarial learning.</p>

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Quantum-adaptive decision intelligence for marketing and production optimization using HamDQN-GL2AN: a hybrid Hamiltonian generative adversarial attention framework

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Sangeetha Radhakrishnan,
  • P. Dency Mary,
  • K. Karthick,
  • G. Teena Jaculin

摘要

In the evolving landscape of data-driven business intelligence, identifying the critical factors influencing marketing and production decisions remains a complex challenge due to the dynamic nature of customer behavior, product bundling strategies, and campaign performance metrics. This study introduces a robust Hamiltonian dense quantum nested generative Lyrebird adversarial attention networks framework leveraging the Marketing-and-Product-Performance-Dataset, which comprises 10,000 records capturing multifaceted dimensions such as revenue generation, subscription tiers, flash sales, bundle dynamics, and post-refund satisfaction. To enhance the decision-making process, data pre-processing is performed using Cumulative Curve Fitting Approximation (CCFA), followed by feature extraction through Dual-Aggregation Transformer (DuAT) that learns causal relationships across features. The final decision-making phase employs the proposed Hamiltonian Dense Quantum Nested Generative Lyrebird Adversarial Attention Network (HamDQN-GL2AN), a novel hybrid model that integrates Hamiltonian Quantum Generative Adversarial Networks (HQuGANs) with Dense Nested Attention Networks (DNA-Net), where learning parameters are optimized using the Lyrebird Optimization Algorithm (LOA). This integrated architecture effectively captures dense quantum interactions and nested attention cues across the dataset, resulting in a highly accurate classification and recommendation system. The classification objective of the proposed framework is to categorize marketing and production performance into predefined decision classes, namely High Performance, Moderate Performance, and Low Performance, based on aggregated business indicators such as conversion rate, revenue generation, ROI, customer satisfaction, and sales effectiveness. The proposed system, therefore, functions as both a predictive classification and business recommendation framework for intelligent marketing and production optimization. The proposed model achieves 99.9% accuracy, demonstrating superior performance in predictive analytics. Advantages of this framework include significantly enhanced sensitivity to nonlinear feature dependencies and reduced decision ambiguity through quantum-aware adversarial learning.