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Research Paper | Computer Science & Engineering | United States of America | Volume 14 Issue 4, April 2025 | Popularity: 5.1 / 10
Quantum-Inspired Adaptive Intelligence Framework for Next-Generation Predictive Systems
Sushma Kukkadapu
Abstract: Predictive systems face increasing complexity and volatility challenges in modern business environments, with traditional methods struggling to adapt without costly retraining. This research introduces a Quantum-Inspired Adaptive Intelligence Framework (QIAIF) that bridges quantum computing principles with classical machine learning to overcome these limitations. The framework leverages quantum-inspired tensor networks for dimensionality reduction, adaptive entanglement-based feature selection, and non-Euclidean representation learning to achieve unprecedented accuracy and computational efficiency. Validated using the public Walmart M5 Forecasting dataset, QIAIF demonstrated a 32.7% accuracy improvement over state-of-the-art deep learning models while reducing computational latency by 59.2%. Most notably, the framework achieved continuous performance improvements under distribution shifts without explicit retraining, recovering from market disruptions within 8 days compared to 21+ days for traditional approaches. These results establish a new direction for predictive intelligence by applying quantum-inspired computational principles to classical systems, with implications for large-scale retail forecasting environments.
Keywords: quantum-inspired computing, predictive intelligence, tensor networks, distribution shift adaptation, non-Euclidean embeddings
Edition: Volume 14 Issue 4, April 2025
Pages: 1068 - 1072
DOI: https://www.doi.org/10.21275/SR25413081739
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