用强化学习精简量子电路中的CNOT门，规模越大越高效。
CNOT Minimal Circuit Synthesis: A Reinforcement Learning Approach
- 单个强化学习智能体处理不同大小的量子电路，通过嵌入或高斯条纹预处理
- 在3到15阶矩阵上测试，规模越大相较现有方法优势越明显
- 适合需要优化量子线路深度的研究者和量子算法开发者
CNOT门是量子计算的基础，用于实现量子纠缠，对量子算法至关重要。某些量子电路完全由CNOT门构成。由于其广泛应用，减少CNOT门数量极为关键。这一问题被称为CNOT最小化，其计算复杂性尚未完全明确。本文提出一种新的强化学习方法解决该问题。不同于为不同电路规模训练多个智能体，我们仅使用一个固定大小m=8的单一智能体，并对非m大小的矩阵采用嵌入或高斯条纹预处理。训练后，在3至15阶矩阵上评估该方法，结果表明随着矩阵规模n增大，本方法性能优于当前最优算法。
原文摘要 · Abstract (English)
CNOT gates are fundamental to quantum computing, as they facilitate entanglement, a crucial resource for quantum algorithms. Certain classes of quantum circuits are constructed exclusively from CNOT gates. Given their widespread use, it is imperative to minimise the number of CNOT gates employed. This problem, known as CNOT minimisation, remains an open challenge, with its computational complexity yet to be fully characterised. In this work, we introduce a novel reinforcement learning approach to address this task. Instead of training multiple reinforcement learning agents for different circuit sizes, we use a single agent up to a fixed size $m$. Matrices of sizes different from m are preprocessed using either embedding or Gaussian striping. To assess the efficacy of our approach, we trained an agent with m = 8, and evaluated it on matrices of size n that range from 3 to 15. The results we obtained show that our method overperforms the state-of-the-art algorithm as the value of n increases.
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