用监督学习加速量子线路合成，效率与成功率双提升
Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
- 用轻量模型预测残差酉矩阵的最小描述长度
- 结合随机束搜索，在多个基准上实现更快合成速度
- 零样本泛化，适合不同量子比特数的电路生成
量子酉矩阵合成旨在将抽象量子算法转化为硬件可执行的量子门序列。由于底层组合搜索空间呈指数增长，精确求解通常不可行。现有方法存在优化目标错位、训练成本高及跨量子比特数泛化能力差等问题。本文通过监督学习近似残差酉矩阵的最小描述长度，并结合随机束搜索，识别出接近最优的门序列。所提方法采用轻量级模型，具备零样本泛化能力，显著降低训练开销。在多个基准测试中，该方法实现了更短的墙钟时间合成速度，且在复杂电路的成功率上超越现有最先进方法。
原文摘要 · Abstract (English)
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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