超越强化学习：快速且可扩展的量子电路合成
Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
AI总结：
本文提出一种基于监督学习和随机束搜索的量子电路合成方法，通过近似最小描述长度实现快速且高效的量子门序列生成，提升了复杂电路的成功率和合成效率。
摘要：
量子单元合成旨在将抽象的量子算法转换为可由硬件执行的量子门序列。由于底层的组合搜索空间呈指数增长，精确解决这一任务通常不可行。现有方法存在优化目标不一致、训练成本高以及在不同量子比特数量上泛化能力有限的问题。我们通过使用监督学习来近似残余单元的最小描述长度，并将此估计与随机束搜索结合，以识别近最优的门序列。我们的方法依赖于一个轻量级模型，具有零样本泛化能力，从而大幅降低了训练成本，与先前基线相比。在多个基准测试中，我们实现了更快的实时时钟合成时间，同时在复杂电路的成功率上超过了最先进的方法。
英文摘要：
Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasible in general due to the exponential growth of the underlying combinatorial search space. Existing approaches suffer from misaligned optimization objectives, substantial training costs and limited generalization across different qubit counts. We mitigate these limitations by using supervised learning to approximate the minimum description length of residual unitaries and combining this estimate with stochastic beam search to identify near optimal gate sequences. Our method relies on a lightweight model with zero-shot generalization, substantially reducing training overhead compared to prior baselines. Across multiple benchmarks, we achieve faster wall-clock synthesis times while exceeding state-of-the-art methods in terms of success rate for complex circuits.
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