Speaker
Description
Variational quantum algorithms are viewed as promising candidates for demon-
strating 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 intro-
duces a novel optimization approach designed to alleviate the adverse effects of BPs
during circuit training. In contrast to conventional gradient descent methods with a
small learning parameter, our approach relies on making a finite hops along the search
direction determined on a randomly chosen subsets of the free parameters. The opti-
mization search direction, together with the range of the search, is determined by 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. Addi-
tionally, 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 applica-
tions, showcasing a significantly improved efficiency in generating highly compressed
quantum circuits compared to traditional gradient-based optimization approaches
