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

How to Cite?: Sushma Kukkadapu, "Quantum-Inspired Adaptive Intelligence Framework for Next-Generation Predictive Systems", Volume 14 Issue 4, April 2025, International Journal of Science and Research (IJSR), Pages: 1068-1072, https://www.ijsr.net/getabstract.php?paperid=SR25413081739, DOI: https://dx.doi.org/10.21275/SR25413081739

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