对比多种量子配置，提升硅原子基态能量计算精度
Benchmarking VQE Configurations: Architectures, Initializations, and Optimizers for Silicon Ground State Energy
- 采用化学启发的量子线路与自适应优化器组合
- 初始化方式显著影响算法稳定性，最优组合收敛更快
- 适合初学者和研究者快速定位高效量子化学模拟设置
量子计算为精确模拟量子化学系统提供了新路径，尤其适用于经典方法难以处理的复杂体系。本文在混合量子-经典优化框架下，研究变分量子本征值求解器（VQE）对硅原子基态能量的估算性能。硅作为较重元素，计算复杂度高。实验中采用双激发门、ParticleConservingU2、UCCSD 和 k-UpCCGSD 等不同量子线路（ansatz），结合梯度下降、SPSA、ADAM 等优化器进行系统性比较。核心贡献在于揭示不同配置间的相互作用机制，建立结构化基准以指导量子化学模拟的参数选择。关键发现：参数初始化对算法稳定性起决定性作用；化学启发型 ansatz 配合自适应优化器，在收敛速度与计算精度上均优于传统方法。
原文摘要 · Abstract (English)
Quantum computing presents a promising path toward precise quantum chemical simulations, particularly for systems that challenge classical methods. This work investigates the performance of the Variational Quantum Eigensolver (VQE) in estimating the ground-state energy of the silicon atom, a relatively heavy element that poses significant computational complexity. Within a hybrid quantum-classical optimization framework, we implement VQE using a range of ansatz, including Double Excitation Gates, ParticleConservingU2, UCCSD, and k-UpCCGSD, combined with various optimizers such as gradient descent, SPSA, and ADAM. The main contribution of this work lies in a systematic methodological exploration of how these configuration choices interact to influence VQE performance, establishing a structured benchmark for selecting optimal settings in quantum chemical simulations. Key findings show that parameter initialization plays a decisive role in the algorithm's stability, and that the combination of a chemically inspired ansatz with adaptive optimization yields superior convergence and precision compared to conventional approaches.
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