发表机构
Gwangju Institute of Science and Technology（光州科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将ma-QAOA用于SYK模型的TFD态制备，提出序列角度剪枝算法简化量子电路，在保持高保真度的同时降低电路深度，为TFD态的噪声量子处理器制备提供了优化方案。
AI 中文摘要
热场双（TFD）态的变分制备往往需要深度量子电路，尤其是针对相互作用多体系统时，在有噪声的量子处理器上制备TFD态时，在保持高保真度的同时简化这些电路至关重要。我们将多角度量子近似优化算法（ma-QAOA）应用于TFD态制备，并提出两种自顶向下的序列角度剪枝算法来研究该问题。从优化后的初始ma-QAOA电路出发，两种算法会依次移除具有小优化角度的泡利串演化项，并在每次移除后重新优化剩余参数。我们将这些算法应用于高斯和二元Sachdev-Ye-Kitaev（SYK）模型的稠密与稀疏情形。研究发现，ma-QAOA可高保真度地制备目标TFD态，且序列小角度剪枝在降低电路深度的同时能保持高保真度，尤其在低温下表现显著；此外，序列小角度剪枝中采用后重新优化的代价函数可进一步提升保真度。对于β=10的二元稀疏N=10 SYK模型，在保留约95%平均保真度的同时，可移除88.8%至92.1%的非局域泡利串演化项。最后，我们提出序列剪枝算法向量子-经典混合实现的扩展方案。
英文摘要
Variational preparation of thermofield double (TFD) states can require deep quantum circuits, particularly for interacting many-body systems. Reducing these circuits while retaining high fidelity is therefore crucial for TFD-state preparation on noisy quantum processors. We study this problem by applying the multi-angle quantum approximate optimization algorithm (ma-QAOA) to TFD-state preparation and introducing two top-down sequential angle-pruning algorithms. Starting from the optimized initial ma-QAOA circuit, both algorithms sequentially remove Pauli-string evolutions with small optimized angles and reoptimize the remaining parameters after each removal. We apply these algorithms to Gaussian and binary Sachdev--Ye--Kitaev (SYK) models in both dense and sparse cases. We find that ma-QAOA prepares the target TFD states with high fidelity and that sequential small-angle pruning retains high fidelity while reducing the circuit depth, particularly at low temperature. Moreover, using the post-reoptimization cost in sequential small-angle pruning further improves the fidelity. For the binary sparse $N=10$ SYK model at $β=10$, $88.8\%$--$92.1\%$ of the nonlocal Pauli-string evolutions are removed while retaining an average fidelity of approximately $95\%$. Finally, we propose extensions of the sequential pruning algorithms toward quantum--classical hybrid implementation.
Comments17 pages, 10 figures
