论文标题
使用加强学习在量子编译器中执行量子路由
Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers
论文作者
论文摘要
“量子路由”是指修改量子电路的任务，以便满足目标量子计算机的连接性约束。这涉及将交换门插入电路中，以便仅在相邻物理量子位之间发生逻辑门。目的是最大程度地减少掉期门增加的电路深度。 在本文中，我们提出了一个使用深Q学习范式的修改版本的量子路由程序。该系统能够从目前在近期体系结构上的两个最先进的量子编译器中胜过两个最先进的量子编译器的量子路由程序。
"Qubit routing" refers to the task of modifying quantum circuits so that they satisfy the connectivity constraints of a target quantum computer. This involves inserting SWAP gates into the circuit so that the logical gates only ever occur between adjacent physical qubits. The goal is to minimise the circuit depth added by the SWAP gates. In this paper, we propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available, on both random and realistic circuits, across near-term architecture sizes.
