Benchmarking Optimizers for Qumode State Preparation with Variational Quantum Algorithms

TL;DR

This paper benchmarks various optimizers for qumode state preparation using Variational Quantum Algorithms, highlighting their performance across different scenarios and identifying the most robust and efficient options.

Contribution

It provides the first comprehensive performance benchmarks of optimizers in qumode state preparation, revealing their strengths and weaknesses in various conditions.

Findings

Powell optimizer is highly robust against sampling errors.

SPSA optimizer handles high-dimensional parameters efficiently.

Some optimizers outperform others depending on the scenario.

Abstract

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.