  Variational Quantum Algorithms (VQAs) employ quantum circuits parameterized
by $U$, optimized using classical methods to minimize a cost function. While
VQAs have found broad applications, certain challenges persist. Notably, a
significant computational burden arises during parameter optimization. The
prevailing ``parameter shift rule'' mandates a double evaluation of the cost
function for each parameter. In this article, we introduce a novel optimization
approach named ``Learning to Learn with an Evolutionary Strategy'' (LLES). LLES
unifies ``Learning to Learn'' and ``Evolutionary Strategy'' methods. ``Learning
to Learn'' treats optimization as a learning problem, utilizing recurrent
neural networks to iteratively propose VQA parameters. Conversely,
``Evolutionary Strategy'' employs gradient searches to estimate function
gradients. Our optimization method is applied to two distinct tasks:
determining the ground state of an Ising Hamiltonian and training a quantum
neural network. Results underscore the efficacy of this novel approach.
Additionally, we identify a key hyperparameter that significantly influences
gradient estimation using the ``Evolutionary Strategy'' method.
