论文标题
通过自适应电路压缩有效的量子门分解
Efficient quantum gate decomposition via adaptive circuit compression
论文作者
论文摘要
在这项工作中，我们根据综合过程中参数两级门的应用报告了一种新型的量子门近似算法。电路设计中这些参数两分门的利用使我们能够将电路合成的离散组合问题转换为连续变量上的优化问题。然后，通过从设计中对两倍的门的顺序去除，将电路压缩，而其余的构建块则通过迭代学习周期不断地适应了降低的栅极结构。我们在“浪费软件包”中实现了开发的算法，并根据几种最先进的量子门合成工具对其进行了基准测试。我们的数值实验表明，我们的编译算法的出色电路压缩能力为大多数地址量子电路提供了最佳的门数。
In this work, we report on a novel quantum gate approximation algorithm based on the application of parametric two-qubit gates in the synthesis process. The utilization of these parametric two-qubit gates in the circuit design allows us to transform the discrete combinatorial problem of circuit synthesis into an optimization problem over continuous variables. The circuit is then compressed by a sequential removal of two-qubit gates from the design, while the remaining building blocks are continuously adapted to the reduced gate structure by iterated learning cycles. We implemented the developed algorithm in the SQUANDER software package and benchmarked it against several state-of-the-art quantum gate synthesis tools. Our numerical experiments revealed outstanding circuit compression capabilities of our compilation algorithm providing the most optimal gate count in the majority of the addressed quantum circuits.
