Qube Engine: Noise-Resilient Quantum Learning Framework for NISQ Systems

This paper introduces Qube Engine, a noise-resilient quantum learning framework designed for NISQ (Noisy Intermediate-Scale Quantum) systems. The framework integrates adaptive quantum circuit optimization, noise-aware training strategies, and hybrid classical-quantum feedback loops to improve stability and performance under realistic hardware constraints. We present a modular architecture that enables efficient encoding of classical data into quantum states while mitigating decoherence effects through dynamic parameter tuning. Experimental simulations demonstrate improved convergence behavior and robustness compared to baseline quantum learning models. Qube Engine aims to bridge the gap between theoretical quantum advantage and practical deployment, offering a scalable pathway toward real-world quantum-enhanced machine learning applications.