论文标题
机器学习优化量子电路布局
Machine Learning Optimization of Quantum Circuit Layouts
论文作者
论文摘要
量子电路布局（QCL）问题是映射量子电路，以便满足设备的约束。我们引入了量子电路映射启发式QXX及其机器学习版本QXX-MLP。后者会自动渗透最佳的QXX参数值，从而使布置电路的深度减小。为了加快电路汇编的速度，在放置电路之前，我们正在使用高斯函数来估计编译电路的深度。该高斯还向编译器通知了电路区域，该电路区域影响了最大的电路深度。我们提供了经验证据，证明使用近似学习布局方法的可行性。 QXX和QXX-MLP为可行的大规模QCL方法打开了路径。
The quantum circuit layout (QCL) problem is to map a quantum circuit such that the constraints of the device are satisfied. We introduce a quantum circuit mapping heuristic, QXX, and its machine learning version, QXX-MLP. The latter infers automatically the optimal QXX parameter values such that the layed out circuit has a reduced depth. In order to speed up circuit compilation, before laying the circuits out, we are using a Gaussian function to estimate the depth of the compiled circuits. This Gaussian also informs the compiler about the circuit region that influences most the resulting circuit's depth. We present empiric evidence for the feasibility of learning the layout method using approximation. QXX and QXX-MLP open the path to feasible large scale QCL methods.
