通过按参数大小选择性激活门，提升量子电路优化能力。
Enhancing Circuit Trainability with Selective Gate Activation Strategy
- 根据参数大小选择性激活量子门，缓解梯度消失问题。
- 该策略使变分量子算法收敛速度提升，优于随机激活等方法。
- 适合研究量子优化与变分量子算法的开发者参考。
混合量子-经典计算严重依赖变分量子算法（VQAs）解决量子化学和机器学习等领域的问题。然而，VQAs面临电路可训练性与表达力之间的平衡难题：可训练性受梯度消失（即荒原峡谷现象）影响，难以优化；而提升表达力通常需更深电路和更多参数，进一步恶化训练难度。本文针对变分量子本征求解器（VQE），研究三种门的激活策略：不考虑门类型或参数大小的随机激活、仅限单一门类型的随机激活，以及基于参数大小的激活。实验结果表明，基于参数大小的策略显著优于其他方法，实现更优收敛性能。
原文摘要 · Abstract (English)
Hybrid quantum-classical computing relies heavily on Variational Quantum Algorithms (VQAs) to tackle challenges in diverse fields like quantum chemistry and machine learning. However, VQAs face a critical limitation: the balance between circuit trainability and expressibility. Trainability, the ease of optimizing circuit parameters for problem-solving, is often hampered by the Barren Plateau, where gradients vanish and hinder optimization. On the other hand, increasing expressibility, the ability to represent a wide range of quantum states, often necessitates deeper circuits with more parameters, which in turn exacerbates trainability issues. In this work, we investigate selective gate activation strategies as a potential solution to these challenges within the context of Variational Quantum Eigensolvers (VQEs). We evaluate three different approaches: activating gates randomly without considering their type or parameter magnitude, activating gates randomly but limited to a single gate type, and activating gates based on the magnitude of their parameter values. Experiment results reveal that the Magnitude-based strategy surpasses other methods, achieving improved convergence.
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