  Variational quantum algorithms are viewed as promising candidates for
demonstrating quantum advantage on near-term devices. These approaches
typically involve the training of parameterized quantum circuits through a
classical optimization loop. However, they often encounter challenges
attributed to the exponentially diminishing gradient components, known as the
barren plateau (BP) problem. This work introduces a novel optimization method
designed to alleviate the adverse effects of BPs during circuit training. Our
approach to select the optimization search direction relies on the distant
features of the cost-function landscape. This enables the optimization path to
navigate around barren plateaus without the need for external control
mechanisms. We have successfully applied our optimization strategy to quantum
circuits comprising $16$ qubits and $15000$ entangling gates, demonstrating
robust resistance against BPs. Additionally, we have extended our optimization
strategy by incorporating an evolutionary selection framework, enhancing its
ability to avoid local minima in the landscape. The modified algorithm has been
successfully utilized in quantum gate synthesis applications, showcasing a
significantly improved efficiency in generating highly compressed quantum
circuits compared to traditional gradient-based optimization approaches.
