-based methods rely on the gradient of the objective function, which is like a slope, to guide the optimization process. However, in the realm of quantum computing, the barren plateau problem occurs when quantum circuits lack a noticeable gradient in the landscape of an optimization problem.
Traditional gradient-based optimization techniques are limited when dealing with barren plateaus. Prior ideas for mitigating barren plateaus, such as quantum natural gradients, and techniques for initializing parameters to avoid weak barren plateaus also have limitations. According to the researchers,"These strategies are expected to optimize quantum circuits in terms of the number of function evaluations and circuit size scaling. The gradient is estimated efficiently with constant evaluations, which remain unaffected by parameter count. Furthermore, these function evaluations are completely independent and can be executed in parallel. This means that these methods are suitable for high-dimensional problems".
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