Gaussian Boson Sampling (GBS) is a model of photonic quantum computing where single-mode squeezed states are sent through linear-optical interferometers and measured using single-photon detectors. In this work, we employ a recent exact sampling algorithm for GBS with threshold detectors to perform classical simulations on the Titan supercomputer. We determine the time and memory resources as well as the amount of computational nodes required to produce samples for different numbers of modes and detector clicks. It is possible to simulate a system with 800 optical modes postselected on outputs with 20 detector clicks, producing a single sample in roughly 2 h using 40% of the available nodes of Titan. Additionally, we benchmark the performance of GBS when applied to dense subgraph identification, even in the presence of photon loss. We perform sampling for several graphs containing as many as 200 vertices. Our findings indicate that large losses can be tolerated and that the use of threshold detectors is preferable over using photon-number-resolving detectors postselected on collision-free outputs.

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泰坦超级计算机上高斯玻色子采样的经典基准测试

高斯玻色子采样（GBS）是光子量子计算的模型，其中单模压缩态通过线性光学干涉仪发送并使用单光子检测器进行测量。在这项工作中，我们使用带有阈值检测器的GBS的最新精确采样算法在Titan超级计算机上执行经典模拟。我们确定时间和内存资源，以及为不同数量的模式和检测器点击产生样本所需的计算节点数量。可以模拟一个具有800个光学模式的系统，该系统在20次检测器咔嗒声后在输出上进行了预选，并使用Titan的40％可用节点在大约2小时内生成了一个样本。此外，即使在存在光子丢失的情况下，我们也将GBS应用于密集子图识别时的性能作为基准。我们对包含多达200个顶点的几个图形执行采样。我们的发现表明，可以容忍较大的损失，并且使用阈值检测器优于使用在无碰撞输出上后选择的光子数解析检测器。