使用变分量子算法进行 Qumode 态制备的优化器基准测试
Benchmarking Optimizers for Qumode State Preparation with Variational Quantum Algorithms
AI总结：
本文针对 qumode 态制备，对变分量子算法中的多种优化器进行多场景基准测试，发现 Powell 抗采样误差能力强，SPSA 在高维参数下更高效。
摘要：
量子态制备是指从初始系统出发制备目标态，这一过程是量子机器学习和求解线性方程组等应用不可或缺的组成部分。近年来，由于该领域的进展及其潜在应用，人们对 qumode 的兴趣不断增长。然而，文献中在专门研究这一方向方面存在明显空白。本文旨在通过对变分量子算法在态制备中使用的各种优化器进行性能基准测试来弥补这一空白。我们在多种场景下开展了大量测试，包括不同的目标态、理想模拟与采样模拟，以及不同数量的基态门层。我们的评估揭示了学习每一类目标态的复杂性，并表明在此情境下某些优化器的表现优于其他优化器。值得注意的是，Powell 优化器对采样误差表现出极强的鲁棒性，因此在容易出现此类误差的场景中成为首选。此外，Simultaneous Perturbation Stochastic Approximation（同时扰动随机逼近）优化器则以其高效性以及有效处理不断增加的参数维度的能力而表现突出。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
