论文标题
具有量子反馈的连贯的ISING机器：总和和有条件的主方程方法
The coherent Ising machine with quantum feedback: the total and conditional master equation methods
论文作者
论文摘要
我们给出了连贯的ISING机器的量子主方程的详细理论推导。这是一个带有反馈的量子计算网络，可以解决NP硬组合问题，包括旅行推销员问题以及各种扩展和类似物。有两种类型的主方程，以反馈电流或无条件为条件。我们表明，可以使用正P相空间表示中的随机方程以可扩展的方式准确模拟两种类型。这取决于存在的非线性，我们使用当前实验的典型参数值。但是，尽管这两种方法非常同意，但在效率方面并不等同。我们发现无条件模拟具有更高的效率，并且可扩展到更大的尺寸。在这种情况下，太多的知识是一件危险的事情。调节反馈电流上的仿真对于确定成功概率并不是必不可少的，而是大大提高了计算复杂性。为了说明使用无条件方法获得的速度提高，我们对具有多达1000个节点的量子主方程进行了完整的量子模拟。
We give a detailed theoretical derivation of the quantum master equation for the coherent Ising machine. This is a quantum computational network with feedback, that solves NP hard combinatoric problems, including the traveling salesman problem and various extensions and analogs. There are two types of master equation that are obtained, either conditional on the feedback current or unconditional. We show that both types can be accurately simulated in a scalable way using stochastic equations in the positive-P phase-space representation. This depends on the nonlinearity present, and we use parameter values that are typical of current experiments. However, while the two approaches are in excellent agreement, they are not equivalent with regard to efficiency. We find that unconditional simulation has much greater efficiency, and is scalable to larger sizes. This is a case where too much knowledge is a dangerous thing. Conditioning the simulations on the feedback current is not essential to determining the success probability, but it greatly increases the computational complexity. To illustrate the speed improvements obtained with the unconditional approach, we carry out full quantum simulations of the quantum master equation with up to 1000 nodes.
