用改进的蒙特卡洛树搜索自动设计高效量子线路，减少门数并提升性能。
Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
- 采用动态采样与渐进扩展策略，优化量子线路搜索空间。
- 相比之前方法，评估次数减少10到100倍，且结果更优或相当。
- 生成线路最多减少3倍CNOT门，适合噪声量子硬件部署。
变分量子算法（VQAs）的性能高度依赖于参数化量子线路的设计。本文提出一种无梯度的蒙特卡洛树搜索（MCTS）方法，用于自动化量子线路设计。该方法引入基于采样方案和渐进扩展技术的新动作空间，实现对搜索空间的动态探索。在随机量子线路任务中，该MCTS方法能有效逼近不同稳定子瑞尼熵值下的非结构化电路，并独立于非稳定性程度逼近基准量子态。该方法在量子化学与线性方程组等多领域均表现稳健。相较于以往研究，本方法将量子线路评估次数减少10至100倍，同时保持或超越原有性能；生成线路的CNOT门数最多减少三倍，显著降低在噪声量子硬件上的实现难度。
原文摘要 · Abstract (English)
The performance of Variational Quantum Algorithms (VQAs) strongly depends on the choice of the parameterized quantum circuit to optimize. One of the biggest challenges in VQAs is designing quantum circuits tailored to the particular problem. This article proposes a gradient-free Monte Carlo Tree Search (MCTS) technique to automate the process of quantum circuit design. Our proposed technique introduces a novel formulation of the action space based on a sampling scheme and a progressive widening technique to explore the space dynamically. When testing our MCTS approach on the domain of random quantum circuits, MCTS approximates unstructured circuits under different values of stabilizer Rényi entropy. It turns out that MCTS manages to approximate the benchmark quantum states independently from their degree of nonstabilizerness. Next, our technique exhibits robustness across various application domains, including quantum chemistry and systems of linear equations. Compared to previous MCTS research, our technique reduces the number of quantum circuit evaluations by a factor of 10 up to 100 while achieving equal or better results. In addition, the resulting quantum circuits exhibit up to three times fewer CNOT gates, which is important for implementation on noisy quantum hardware.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。