Dynamic Control of Information Scrambling: High-Frequency Parametric Mitigation of Barren Plateaus in QML

This work presents a theoretical framework for mitigating the barren plateau problem in Variational Quantum Algorithms (VQAs) and Quantum Reinforcement Learning (QRL) through high-frequency parametric modulation. Using Floquet averaging and the Magnus expansion, we show that periodic high-frequency modulation of entangling generators induces an effective renormalization of the circuit dynamics, dressing the interaction strength via a zeroth-order Bessel function dependence. At a critical modulation amplitude, the effective entangling rate is strongly suppressed, restricting chaotic information scrambling and delaying the onset of the Haar-random regime. This leads to a phenomenological crossover in gradient variance, enhancing trainability in intermediate-depth circuits. The framework is consistent with known results in Floquet engineering and provides a dynamic mechanism to control scrambling in quantum machine learning systems. A falsifiable experimental protocol on superconducting NISQ processors is proposed to test the predicted threshold behavior and gradient enhancement.