  Quantum state preparation involves preparing a target state from an initial
system, a process integral to applications such as quantum machine learning and
solving systems of linear equations. Recently, there has been a growing
interest in qumodes due to advancements in the field and their potential
applications. However there is a notable gap in the literature specifically
addressing this area. This paper aims to bridge this gap by providing
performance benchmarks of various optimizers used in state preparation with
Variational Quantum Algorithms. We conducted extensive testing across multiple
scenarios, including different target states, both ideal and sampling
simulations, and varying numbers of basis gate layers. Our evaluations offer
insights into the complexity of learning each type of target state and
demonstrate that some optimizers perform better than others in this context.
Notably, the Powell optimizer was found to be exceptionally robust against
sampling errors, making it a preferred choice in scenarios prone to such
inaccuracies. Additionally, the Simultaneous Perturbation Stochastic
Approximation optimizer was distinguished for its efficiency and ability to
handle increased parameter dimensionality effectively.
