用强化学习优化量子线路预合成，减少昂贵的T门数量。
Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce $T$-count
- 通过强化学习选择局部等效变换序列，优化线路结构
- 在25量子比特电路上实现最高20%的T门数减少
- 适用于需要降低容错量子计算成本的研究者
将量子线路编译为克利福德+T门是使用稳定子码进行容错量子计算的核心任务。短期内，T门将主导容错实现的成本，任何对这类昂贵门数量的减少都可能决定电路能否运行。尽管精确合成在量子比特数上呈指数困难，但通常采用局部合成方法将大电路分解为子结构进行编译。然而，局部方法的组合会导致关键指标（如T门数或电路深度）次优，且性能强烈依赖于电路表示。本文提出 extsc{Q-PreSyn}策略：在一组保持电路等价性的局部编辑操作基础上，利用强化学习代理识别有效操作序列，从而获得在后续合成中可降低T门数的电路表示。实验结果表明，在知名合成算法之上应用该策略，可在最多25量子比特的电路上实现高达20%的T门数减少，且合成前无额外近似误差。
原文摘要 · Abstract (English)
Compiling quantum circuits into Clifford+$T$ gates is a central task for fault-tolerant quantum computing using stabilizer codes. In the near term, $T$ gates will dominate the cost of fault tolerant implementations, and any reduction in the number of such expensive gates could mean the difference between being able to run a circuit or not. While exact synthesis is exponentially hard in the number of qubits, local synthesis approaches are commonly used to compile large circuits by decomposing them into substructures. However, composing local methods leads to suboptimal compilations in key metrics such as $T$-count or circuit depth, and their performance strongly depends on circuit representation. In this work, we address this challenge by proposing \textsc{Q-PreSyn}, a strategy that, given a set of local edits preserving circuit equivalence, uses a RL agent to identify effective sequences of such actions and thereby obtain circuit representations that yield a reduced $T$-count upon synthesis. Experimental results of our proposed strategy, applied on top of well-known synthesis algorithms, show up to a $20\%$ reduction in $T$-count on circuits with up to 25 qubits, without introducing any additional approximation error prior to synthesis.
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