The vast body of contemporary quantum algorithms that demonstrate quantum computers’ performance advantages mainly concerns the optimization area. This fact has a significant impact. Indeed, optimization is the central task in a wide range of technology fields and in human activities where a sequence of decisions, each significantly influencing the problem value, is made. In Quantum Machine Learning (QML), the optimization associated with the isomorphism of quantum physical models into the sought artificial intelligence models becomes more and more important. In this context, we present the enhanced vanishing-exploding gradient problem-solving optimization in QML. We reformulate training through direct optimization on pixel-valued functions, remarkably similar to the approach in Ridge Regression, and then propose an unfolding scheme enabling the elevated turn of vanilla gradient descent. Such gradient descent is comparable with its quantum optimization analog commonly used under the name of solving the Schrödinger equation and also largely allows diminishing the hope domain for quantum advantage. In numerical experiments, our proposal successfully learns high-fidelity non-linear embeddings up to the dimension of the training set. This study addresses the optimization problem no longer as an auxiliary stage but as a crucial component in emerging QML. More precisely, we aim to noticeably enhance the achieving process of one of the most promising QML approaches today. According to its original description, this approach makes the isomorphism between quantum physical models and the sought artificial intelligence models.