用强化学习优化量子电路，减少关键的CNOT门数量。
Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
- 结合强化学习与图神经网络，直接在ZX图上搜索最优变换路径。
- 在随机电路测试中，比现有方法减少最多40%的CNOT门数。
- 适合关注量子纠错与硬件效率的研究者和工程师。
当前量子计算受噪声严重影响，尤其是两量子比特门引入的误差。因此，在嘈杂中等规模量子硬件上，减少两量子比特门数量至关重要。本文提出一种基于ZX微积分、图神经网络与强化学习的量子电路优化框架。通过强化学习与树搜索结合，解决如何选择最优的ZX微积分重写规则这一难题。不同于依赖预设启发式规则的方法，本方法训练新型强化学习策略，直接作用于ZX图，从而在所有可能的电路变换空间中搜索，显著减少CNOT门数量。该方法可突破硬编码规则限制，发现任意优化规则。实验表明，该方法在大规模多样化随机电路上具备竞争力与泛化能力，优于现有先进优化器。
原文摘要 · Abstract (English)
Quantum computing is currently strongly limited by the impact of noise, in particular introduced by the application of two-qubit gates. For this reason, reducing the number of two-qubit gates is of paramount importance on noisy intermediate-scale quantum hardware. To advance towards more reliable quantum computing, we introduce a framework based on ZX calculus, graph-neural networks and reinforcement learning for quantum circuit optimization. By combining reinforcement learning and tree search, our method addresses the challenge of selecting optimal sequences of ZX calculus rewrite rules. Instead of relying on existing heuristic rules for minimizing circuits, our method trains a novel reinforcement learning policy that directly operates on ZX-graphs, therefore allowing us to search through the space of all possible circuit transformations to find a circuit significantly minimizing the number of CNOT gates. This way we can scale beyond hard-coded rules towards discovering arbitrary optimization rules. We demonstrate our method's competetiveness with state-of-the-art circuit optimizers and generalization capabilities on large sets of diverse random circuits.
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