Regularizing Quantum Gradient Flows via Non-Markovian K-Kernel Integration: A Solution to Barren Plateaus in Variational Quantum Algorithms
Year: 2026
Type: preprint
Abstract: Quantum Machine Learning (QML) and Variational Quantum Algorithms (VQAs) currently suffer from the "Barren Plateau" phenomenon, where the gradient of the cost function vanishes exponentially with the number of qubits, halting the optimization process. In this work, we introduce a novel regularization approach based on the Adaptive Memory-Decay Control Framework (AMDCF). By integrating the non-local K-Kernel memory field into the quantum parameter updates, we smooth the gradient flow and prevent the vanishing gradient problem. We simulate a quantum feature map and demonstrate that the K-regularized gradient remains stable and bounded, in stark contrast to the standard QML gradient which collapses into noise. This establishes the K-Kernel as a vital optimization tool for the NISQ era and quantum supremacy.
Author: SAIM, MOHAMMED TAHAR
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225324246