发表机构
Google Quantum AI; Stanford University; MIT; Institute for Quantum Computing, University of Waterloo and Perimeter Institute for Theoretical Physics（谷歌量子人工智能; 斯坦福大学; 麻省理工学院; 滑铁卢大学量子计算研究所与佩里尔理论物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出利用适应性编译方法，将多项式规模量子电路的编译开销从对数级别降至常数，实现最优的逆多项式误差近似。
AI 中文摘要
将连续门集编译到离散通用门集通常会产生开销。由$G$个任意单比特和双比特门组成的电路，可以通过用$O(\log(G/\epsilon))$个基本门的序列替换每个原始门，从而被$\{H,T,\mathrm{CNOT}\}$中的门序列$\epsilon$-近似。我们证明，对于具有逆多项式目标误差的多项式规模电路，利用适应性可以避免这种开销，这是最优的。
英文摘要
Compiling a continuous gate set to a discrete universal gate set generally incurs an overhead. A circuit composed of $G$ arbitrary one- and two-qubit gates can be $ε$-approximated by a sequence of gates from $\{H,T,\mathrm{CNOT}\}$ by replacing each original gate by a sequence of $O(\log(G/ε))$ elementary gates. We show that this overhead can be avoided using adaptivity for polynomial-sized circuits with inverse-polynomial target error, which is optimal.
