标题： 通过使用强化学习进行对角化实现高精度容错量子电路综合 显示英文标题

标题： High Precision Fault-Tolerant Quantum Circuit Synthesis by Diagonalization using Reinforcement Learning

摘要： 高效且高精度地将程序编译为容错门集（如Clifford+T门集）中的量子电路，对于量子计算的成功至关重要。 已知对于限制类的酉矩阵，有最优的解析编译方法，否则该问题是难以处理的。 基于经验搜索的综合方法，包括强化学习和模拟退火，可以为更广泛的酉矩阵生成良好的实现，但需要在近似精度和资源使用之间进行权衡。 我们利用基于搜索的方法将一般的酉矩阵综合问题转化为综合对角酉矩阵的问题；这个问题在一般情况下可以高效解决，在单量子比特情况下可以最优解决。 我们展示了我们的方法如何在来自实际量子算法的一组酉矩阵上提高容错综合算法可达到的实现精度。 在这些基准测试中，其中许多现有方法无法处理，我们观察到与更通用的量子香农分解相比，资源密集型非Clifford门平均减少了95%。 在对未来短期应用感兴趣的算法子集上，与其它方法相比，对角化可以将T门数量减少多达16.8%。 显示英文摘要

摘要： Resource efficient and high precision compilation of programs into quantum circuits expressed in Fault-Tolerant gate sets, such as the Clifford+T gate set, is vital for the success of quantum computing. Optimal analytical compilation methods are known for restricted classes of unitaries, otherwise the problem is intractable. Empirical search-based synthesis methods, including Reinforcement Learning and simulated annealing, can generate good implementations for a more extensive set of unitaries, but require trade-offs in approximation precision and resource use. We leverage search-based methods to reduce the general unitary synthesis problem to one of synthesizing diagonal unitaries; a problem solvable efficiently in general and optimally in the single-qubit case. We demonstrate how our approach improves the implementation precision attainable by Fault-Tolerant synthesis algorithms on an array of unitaries taken from real quantum algorithms. On these benchmarks, many of which cannot be handled by existing approaches, we observe an average of 95% fewer resource-intensive non-Clifford gates compared to the more general Quantum Shannon Decomposition. On a subset of algorithms of interest for future term applications, diagonalization can reduce T gate counts by up to 16.8% compared to other methods.