Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit. Due to decoherence, the quality of the computational outcome can degrade seriously with increasing circuit length. Thus, there is major interest to reduce a quantum circuit to an equivalent circuit which is in its gate count as short as possible. One method to address efficient transpilation is based on approaches known from stochastic optimization, e.g. by using random sampling and token replacement strategies. Here, a core challenge is that these methods can suffer from sampling efficiency, causing long and energy consuming optimization time. As a remedy, we propose in this work 2D neural guided sampling. Thus, given a 2D representation of a quantum circuit, a neural network predicts groups of gates in the quantum circuit, which are likely reducible. Thus, it leads to a sampling prior which can heavily reduce the compute time for quantum circuit reduction. In several experiments, we demonstrate that our method is superior to results obtained from different qiskit or BQSKit optimization levels.

翻译：将通用量子电路转换为特定硬件拓扑结构下可用门集受限的等效电路（即量子电路编译）通常会导致等效电路长度显著增加。由于退相干效应，计算结果的保真度会随电路长度增加而严重下降。因此，如何将量子电路优化为门数量尽可能少的等效电路成为关键研究课题。当前一种高效的编译方法借鉴了随机优化思想，例如采用随机采样与令牌替换策略。然而这类方法面临的核心挑战在于采样效率问题，可能导致优化过程耗时且能耗巨大。为此，本研究提出二维神经引导采样方法：通过量子电路的二维表征，神经网络可预测电路中可能被简化的门组集合，从而构建能大幅缩短量子电路化简计算时间的采样先验分布。系列实验表明，本方法在优化效果上优于不同层级的qiskit与BQSKit优化方案。