Accelerating AI Convergence: Hybrid Quantum-Classical Framework for Robust Decision Support in Learning Systems

The slow convergence of deep learning systems on high-curvature optimization surfaces and computational constraints of GPU-based inference methods are constraints to training deep learning systems at scale. The hybrid quantum–classical framework reduces iteration complexity and stabilizes decision outputs. Its architecture has a variational quantum circuit (8 qubits, depth=6) incorporated in a classical transformer-based backbone and uses gated fusion to mix adaptively represented mixtures. A 1.5M-sample structured decision dataset and a 220k-instance high-dimensional benchmark trial demonstrate a 34.7% decrease in iterations to ε=10⁻³ when using Adam and a 21.3% decrease in wall-clock training time under the same batch constraints (batch=256). Under stochastic perturbation stability variance reduces by 18.5%. Latency of inference on GPU backed simulation is not more than 7.4 ms per instance. The findings show that there is an acceleration that can be measured without deterioration of calibration error.